Store DLL info in mkinmod objects for performance

Thanks to Tomas Kalibera for his analysis of the problem on the r-package-devel mailing list and for the suggestion on how to fix it. See the current benchmark vignette for the new data on mkin 1.1.1 with R 4.2.1, with unprecedented performance.
author: Johannes Ranke <jranke@uni-bremen.de> 2022-07-08 17:39:44 +0200
committer: Johannes Ranke <jranke@uni-bremen.de> 2022-07-08 17:39:44 +0200
commit: f35e0b3d3b9f41bee2f5cc357afcb69e3aadad15 (patch)
tree: 675d90c517a0e8a32c7c3af8ef631a5c588503d4
parent: 16a7ed9548b37fe3c68c993651226fdc2dda6402 (diff)
25 files changed, 1952 insertions, 2157 deletions
diff --git a/DESCRIPTION b/DESCRIPTION
index b8dd37eb..569fd2c7 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: mkin
 Type: Package
 Title: Kinetic Evaluation of Chemical Degradation Data
-Version: 1.1.0
-Date: 2022-04-09
+Version: 1.1.1
+Date: 2022-07-08
 Authors@R: c(
   person("Johannes", "Ranke", role = c("aut", "cre", "cph"),
     email = "johannes.ranke@jrwb.de",
diff --git a/NEWS.md b/NEWS.md
index ee83738e..2aba0881 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,7 @@
 # mkin 1.1.1
 
+- 'R/{mkinmod,mkinpredict}.R: Store DLL information in mkinmod objects and use that information in mkinpredict to avoid a performance regression brought by a bugfix in R 4.2.x. Thanks to Tomas Kalibera for his analysis of the problem on the r-package-devel list and his suggestion on how to fix it.
+
 - 'vignettes/FOCUS_L.rmd': Remove an outdated note referring to a failure to calculate the covariance matrix for DFOP with the L2 dataset. Since 0.9.45.5 the covariance matrix is available
 
 - 'vignettes/web_only/benchmarks.rmd': Add the first benchmark data using my laptop system, therefore add the CPU when showing the benchmark results.
diff --git a/R/mkinmod.R b/R/mkinmod.R
index 071f9e0e..d8740aed 100644
--- a/R/mkinmod.R
+++ b/R/mkinmod.R
@@ -464,9 +464,9 @@ mkinmod <- function(..., use_of_ff = "max", name = NULL,
     if (!inherits(model$cf, "try-error")) {
       if (is.null(dll_dir)) {
         if (!quiet) message("Temporary DLL for differentials generated and loaded")
-        dll_info <- inline::getDynLib(model$cf)
+        model$dll_info <- inline::getDynLib(model$cf)
       } else {
-        dll_info <- inline::moveDLL(model$cf, name, dll_dir,
+        model$dll_info <- inline::moveDLL(model$cf, name, dll_dir,
           unload = unload, overwrite = overwrite, verbose = !quiet)
       }
     }
diff --git a/R/mkinpredict.R b/R/mkinpredict.R
index 3402a7ba..9e6ba31f 100644
--- a/R/mkinpredict.R
+++ b/R/mkinpredict.R
@@ -177,7 +177,8 @@ mkinpredict.mkinmod <- function(x,
         times = outtimes,
         func = "diffs",
         initfunc = "initpar",
-        dllname = inline::getDynLib(x$cf)[["name"]],
+        dllname = if (is.null(x$dll_info)) inline::getDynLib(x$cf)[["name"]]
+          else x$dll_info[["name"]],
         parms = odeparms[x$parms], # Order matters when using compiled models
         method = method.ode,
         atol = atol,
diff --git a/build.log b/build.log
index c88520ce..237294e7 100644
--- a/build.log
+++ b/build.log
@@ -1,10 +1,9 @@
 * checking for file ‘./DESCRIPTION’ ... OK
 * preparing ‘mkin’:
 * checking DESCRIPTION meta-information ... OK
-* installing the package to process help pages
-* saving partial Rd database
+* installing the package to build vignettes
 * creating vignettes ... OK
 * checking for LF line-endings in source and make files and shell scripts
 * checking for empty or unneeded directories
-* building ‘mkin_1.1.0.tar.gz’
+* building ‘mkin_1.1.1.tar.gz’
 
diff --git a/docs/404.html b/docs/404.html
index 6d48c164..7a6ede72 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -32,7 +32,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index d3918ef4..7d36c77c 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -33,7 +33,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
@@ -105,7 +105,7 @@
       <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
                         <h4 data-toc-skip class="author">Johannes Ranke</h4>
             
-            <h4 data-toc-skip class="date">Last change 18 May 2022 (rebuilt 2022-06-30)</h4>
+            <h4 data-toc-skip class="date">Last change 18 May 2022 (rebuilt 2022-07-08)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_L.rmd" class="external-link"><code>vignettes/FOCUS_L.rmd</code></a></small>
       <div class="hidden name"><code>FOCUS_L.rmd</code></div>
@@ -119,197 +119,189 @@
 </h2>
 <p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="va">FOCUS_2006_L1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>
-  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">21</span>, <span class="fl">30</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,
-  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,
-             <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,
-             <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span><span class="op">)</span><span class="op">)</span>
-<span class="va">FOCUS_2006_L1_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L1</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="va">FOCUS_2006_L1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
+<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">21</span>, <span class="fl">30</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
+<span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span>
+<span>             <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span>
+<span>             <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">FOCUS_2006_L1_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L1</span><span class="op">)</span></span></code></pre></div>
 <p>Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the models, please see the FOCUS kinetics report.</p>
 <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">m.L1.SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></code></pre></div>
-<pre><code><span class="co">## mkin version used for fitting:    1.1.0 </span>
-<span class="co">## R version used for fitting:       4.2.1 </span>
-<span class="co">## Date of fit:     Thu Jun 30 10:43:59 2022 </span>
-<span class="co">## Date of summary: Thu Jun 30 10:43:59 2022 </span>
-<span class="co">## </span>
-<span class="co">## Equations:</span>
-<span class="co">## d_parent/dt = - k_parent * parent</span>
-<span class="co">## </span>
-<span class="co">## Model predictions using solution type analytical </span>
-<span class="co">## </span>
-<span class="co">## Fitted using 133 model solutions performed in 0.032 s</span>
-<span class="co">## </span>
-<span class="co">## Error model: Constant variance </span>
-<span class="co">## </span>
-<span class="co">## Error model algorithm: OLS </span>
-<span class="co">## </span>
-<span class="co">## Starting values for parameters to be optimised:</span>
-<span class="co">##          value   type</span>
-<span class="co">## parent_0 89.85  state</span>
-<span class="co">## k_parent  0.10 deparm</span>
-<span class="co">## </span>
-<span class="co">## Starting values for the transformed parameters actually optimised:</span>
-<span class="co">##                  value lower upper</span>
-<span class="co">## parent_0     89.850000  -Inf   Inf</span>
-<span class="co">## log_k_parent -2.302585  -Inf   Inf</span>
-<span class="co">## </span>
-<span class="co">## Fixed parameter values:</span>
-<span class="co">## None</span>
-<span class="co">## </span>
-<span class="co">## Results:</span>
-<span class="co">## </span>
-<span class="co">##        AIC     BIC    logLik</span>
-<span class="co">##   93.88778 96.5589 -43.94389</span>
-<span class="co">## </span>
-<span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="co">##              Estimate Std. Error  Lower  Upper</span>
-<span class="co">## parent_0       92.470    1.28200 89.740 95.200</span>
-<span class="co">## log_k_parent   -2.347    0.03763 -2.428 -2.267</span>
-<span class="co">## sigma           2.780    0.46330  1.792  3.767</span>
-<span class="co">## </span>
-<span class="co">## Parameter correlation:</span>
-<span class="co">##                parent_0 log_k_parent      sigma</span>
-<span class="co">## parent_0      1.000e+00    6.186e-01 -1.516e-09</span>
-<span class="co">## log_k_parent  6.186e-01    1.000e+00 -3.124e-09</span>
-<span class="co">## sigma        -1.516e-09   -3.124e-09  1.000e+00</span>
-<span class="co">## </span>
-<span class="co">## Backtransformed parameters:</span>
-<span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="co">## for estimators of untransformed parameters.</span>
-<span class="co">##          Estimate t value    Pr(&gt;t)    Lower   Upper</span>
-<span class="co">## parent_0 92.47000   72.13 8.824e-21 89.74000 95.2000</span>
-<span class="co">## k_parent  0.09561   26.57 2.487e-14  0.08824  0.1036</span>
-<span class="co">## sigma     2.78000    6.00 1.216e-05  1.79200  3.7670</span>
-<span class="co">## </span>
-<span class="co">## FOCUS Chi2 error levels in percent:</span>
-<span class="co">##          err.min n.optim df</span>
-<span class="co">## All data   3.424       2  7</span>
-<span class="co">## parent     3.424       2  7</span>
-<span class="co">## </span>
-<span class="co">## Estimated disappearance times:</span>
-<span class="co">##         DT50  DT90</span>
-<span class="co">## parent 7.249 24.08</span>
-<span class="co">## </span>
-<span class="co">## Data:</span>
-<span class="co">##  time variable observed predicted residual</span>
-<span class="co">##     0   parent     88.3    92.471  -4.1710</span>
-<span class="co">##     0   parent     91.4    92.471  -1.0710</span>
-<span class="co">##     1   parent     85.6    84.039   1.5610</span>
-<span class="co">##     1   parent     84.5    84.039   0.4610</span>
-<span class="co">##     2   parent     78.9    76.376   2.5241</span>
-<span class="co">##     2   parent     77.6    76.376   1.2241</span>
-<span class="co">##     3   parent     72.0    69.412   2.5884</span>
-<span class="co">##     3   parent     71.9    69.412   2.4884</span>
-<span class="co">##     5   parent     50.3    57.330  -7.0301</span>
-<span class="co">##     5   parent     59.4    57.330   2.0699</span>
-<span class="co">##     7   parent     47.0    47.352  -0.3515</span>
-<span class="co">##     7   parent     45.1    47.352  -2.2515</span>
-<span class="co">##    14   parent     27.7    24.247   3.4528</span>
-<span class="co">##    14   parent     27.3    24.247   3.0528</span>
-<span class="co">##    21   parent     10.0    12.416  -2.4163</span>
-<span class="co">##    21   parent     10.4    12.416  -2.0163</span>
-<span class="co">##    30   parent      2.9     5.251  -2.3513</span>
-<span class="co">##    30   parent      4.0     5.251  -1.2513</span></code></pre>
+<code class="sourceCode R"><span><span class="va">m.L1.SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.1.1 </span></span>
+<span><span class="co">## R version used for fitting:       4.2.1 </span></span>
+<span><span class="co">## Date of fit:     Fri Jul  8 17:34:00 2022 </span></span>
+<span><span class="co">## Date of summary: Fri Jul  8 17:34:00 2022 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Equations:</span></span>
+<span><span class="co">## d_parent/dt = - k_parent * parent</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model predictions using solution type analytical </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fitted using 133 model solutions performed in 0.028 s</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model: Constant variance </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model algorithm: OLS </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for parameters to be optimised:</span></span>
+<span><span class="co">##          value   type</span></span>
+<span><span class="co">## parent_0 89.85  state</span></span>
+<span><span class="co">## k_parent  0.10 deparm</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
+<span><span class="co">##                  value lower upper</span></span>
+<span><span class="co">## parent_0     89.850000  -Inf   Inf</span></span>
+<span><span class="co">## log_k_parent -2.302585  -Inf   Inf</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fixed parameter values:</span></span>
+<span><span class="co">## None</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Results:</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##        AIC     BIC    logLik</span></span>
+<span><span class="co">##   93.88778 96.5589 -43.94389</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
+<span><span class="co">##              Estimate Std. Error  Lower  Upper</span></span>
+<span><span class="co">## parent_0       92.470    1.28200 89.740 95.200</span></span>
+<span><span class="co">## log_k_parent   -2.347    0.03763 -2.428 -2.267</span></span>
+<span><span class="co">## sigma           2.780    0.46330  1.792  3.767</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parameter correlation:</span></span>
+<span><span class="co">##                parent_0 log_k_parent      sigma</span></span>
+<span><span class="co">## parent_0      1.000e+00    6.186e-01 -1.712e-09</span></span>
+<span><span class="co">## log_k_parent  6.186e-01    1.000e+00 -3.237e-09</span></span>
+<span><span class="co">## sigma        -1.712e-09   -3.237e-09  1.000e+00</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Backtransformed parameters:</span></span>
+<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
+<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
+<span><span class="co">## for estimators of untransformed parameters.</span></span>
+<span><span class="co">##          Estimate t value    Pr(&gt;t)    Lower   Upper</span></span>
+<span><span class="co">## parent_0 92.47000   72.13 8.824e-21 89.74000 95.2000</span></span>
+<span><span class="co">## k_parent  0.09561   26.57 2.487e-14  0.08824  0.1036</span></span>
+<span><span class="co">## sigma     2.78000    6.00 1.216e-05  1.79200  3.7670</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
+<span><span class="co">##          err.min n.optim df</span></span>
+<span><span class="co">## All data   3.424       2  7</span></span>
+<span><span class="co">## parent     3.424       2  7</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Estimated disappearance times:</span></span>
+<span><span class="co">##         DT50  DT90</span></span>
+<span><span class="co">## parent 7.249 24.08</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Data:</span></span>
+<span><span class="co">##  time variable observed predicted residual</span></span>
+<span><span class="co">##     0   parent     88.3    92.471  -4.1710</span></span>
+<span><span class="co">##     0   parent     91.4    92.471  -1.0710</span></span>
+<span><span class="co">##     1   parent     85.6    84.039   1.5610</span></span>
+<span><span class="co">##     1   parent     84.5    84.039   0.4610</span></span>
+<span><span class="co">##     2   parent     78.9    76.376   2.5241</span></span>
+<span><span class="co">##     2   parent     77.6    76.376   1.2241</span></span>
+<span><span class="co">##     3   parent     72.0    69.412   2.5884</span></span>
+<span><span class="co">##     3   parent     71.9    69.412   2.4884</span></span>
+<span><span class="co">##     5   parent     50.3    57.330  -7.0301</span></span>
+<span><span class="co">##     5   parent     59.4    57.330   2.0699</span></span>
+<span><span class="co">##     7   parent     47.0    47.352  -0.3515</span></span>
+<span><span class="co">##     7   parent     45.1    47.352  -2.2515</span></span>
+<span><span class="co">##    14   parent     27.7    24.247   3.4528</span></span>
+<span><span class="co">##    14   parent     27.3    24.247   3.0528</span></span>
+<span><span class="co">##    21   parent     10.0    12.416  -2.4163</span></span>
+<span><span class="co">##    21   parent     10.4    12.416  -2.0163</span></span>
+<span><span class="co">##    30   parent      2.9     5.251  -2.3513</span></span>
+<span><span class="co">##    30   parent      4.0     5.251  -1.2513</span></span></code></pre>
 <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - SFO"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - SFO"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-4-1.png" width="576"></p>
 <p>The residual plot can be easily obtained by</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="../reference/mkinresplot.html">mkinresplot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, ylab <span class="op">=</span> <span class="st">"Observed"</span>, xlab <span class="op">=</span> <span class="st">"Time"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/mkinresplot.html">mkinresplot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, ylab <span class="op">=</span> <span class="st">"Observed"</span>, xlab <span class="op">=</span> <span class="st">"Time"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-5-1.png" width="576"></p>
 <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">m.L1.FOMC</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
-<pre><code><span class="co">## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:</span>
-<span class="co">## false convergence (8)</span></code></pre>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - FOMC"</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span><span class="va">m.L1.FOMC</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - FOMC"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-6-1.png" width="576"></p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
-<pre><code><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></code></pre>
-<pre><code><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></code></pre>
-<pre><code><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span>
-<span class="co">## doubtful</span></code></pre>
-<pre><code><span class="co">## mkin version used for fitting:    1.1.0 </span>
-<span class="co">## R version used for fitting:       4.2.1 </span>
-<span class="co">## Date of fit:     Thu Jun 30 10:44:00 2022 </span>
-<span class="co">## Date of summary: Thu Jun 30 10:44:00 2022 </span>
-<span class="co">## </span>
-<span class="co">## Equations:</span>
-<span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
-<span class="co">## </span>
-<span class="co">## Model predictions using solution type analytical </span>
-<span class="co">## </span>
-<span class="co">## Fitted using 369 model solutions performed in 0.082 s</span>
-<span class="co">## </span>
-<span class="co">## Error model: Constant variance </span>
-<span class="co">## </span>
-<span class="co">## Error model algorithm: OLS </span>
-<span class="co">## </span>
-<span class="co">## Starting values for parameters to be optimised:</span>
-<span class="co">##          value   type</span>
-<span class="co">## parent_0 89.85  state</span>
-<span class="co">## alpha     1.00 deparm</span>
-<span class="co">## beta     10.00 deparm</span>
-<span class="co">## </span>
-<span class="co">## Starting values for the transformed parameters actually optimised:</span>
-<span class="co">##               value lower upper</span>
-<span class="co">## parent_0  89.850000  -Inf   Inf</span>
-<span class="co">## log_alpha  0.000000  -Inf   Inf</span>
-<span class="co">## log_beta   2.302585  -Inf   Inf</span>
-<span class="co">## </span>
-<span class="co">## Fixed parameter values:</span>
-<span class="co">## None</span>
-<span class="co">## </span>
-<span class="co">## </span>
-<span class="co">## Warning(s): </span>
-<span class="co">## Optimisation did not converge:</span>
-<span class="co">## false convergence (8)</span>
-<span class="co">## </span>
-<span class="co">## Results:</span>
-<span class="co">## </span>
-<span class="co">##        AIC      BIC   logLik</span>
-<span class="co">##   95.88781 99.44929 -43.9439</span>
-<span class="co">## </span>
-<span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="co">##           Estimate Std. Error  Lower  Upper</span>
-<span class="co">## parent_0     92.47     1.2820 89.720 95.220</span>
-<span class="co">## log_alpha    13.78        NaN    NaN    NaN</span>
-<span class="co">## log_beta     16.13        NaN    NaN    NaN</span>
-<span class="co">## sigma         2.78     0.4598  1.794  3.766</span>
-<span class="co">## </span>
-<span class="co">## Parameter correlation:</span>
-<span class="co">##            parent_0 log_alpha log_beta     sigma</span>
-<span class="co">## parent_0  1.0000000       NaN      NaN 0.0001671</span>
-<span class="co">## log_alpha       NaN         1      NaN       NaN</span>
-<span class="co">## log_beta        NaN       NaN        1       NaN</span>
-<span class="co">## sigma     0.0001671       NaN      NaN 1.0000000</span>
-<span class="co">## </span>
-<span class="co">## Backtransformed parameters:</span>
-<span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="co">## for estimators of untransformed parameters.</span>
-<span class="co">##           Estimate t value Pr(&gt;t)  Lower  Upper</span>
-<span class="co">## parent_0 9.247e+01      NA     NA 89.720 95.220</span>
-<span class="co">## alpha    9.658e+05      NA     NA     NA     NA</span>
-<span class="co">## beta     1.010e+07      NA     NA     NA     NA</span>
-<span class="co">## sigma    2.780e+00      NA     NA  1.794  3.766</span>
-<span class="co">## </span>
-<span class="co">## FOCUS Chi2 error levels in percent:</span>
-<span class="co">##          err.min n.optim df</span>
-<span class="co">## All data   3.619       3  6</span>
-<span class="co">## parent     3.619       3  6</span>
-<span class="co">## </span>
-<span class="co">## Estimated disappearance times:</span>
-<span class="co">##        DT50  DT90 DT50back</span>
-<span class="co">## parent 7.25 24.08     7.25</span></code></pre>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre>
+<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre>
+<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span>
+<span><span class="co">## doubtful</span></span></code></pre>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.1.1 </span></span>
+<span><span class="co">## R version used for fitting:       4.2.1 </span></span>
+<span><span class="co">## Date of fit:     Fri Jul  8 17:34:00 2022 </span></span>
+<span><span class="co">## Date of summary: Fri Jul  8 17:34:00 2022 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Equations:</span></span>
+<span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model predictions using solution type analytical </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fitted using 357 model solutions performed in 0.07 s</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model: Constant variance </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model algorithm: OLS </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for parameters to be optimised:</span></span>
+<span><span class="co">##          value   type</span></span>
+<span><span class="co">## parent_0 89.85  state</span></span>
+<span><span class="co">## alpha     1.00 deparm</span></span>
+<span><span class="co">## beta     10.00 deparm</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
+<span><span class="co">##               value lower upper</span></span>
+<span><span class="co">## parent_0  89.850000  -Inf   Inf</span></span>
+<span><span class="co">## log_alpha  0.000000  -Inf   Inf</span></span>
+<span><span class="co">## log_beta   2.302585  -Inf   Inf</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fixed parameter values:</span></span>
+<span><span class="co">## None</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Results:</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##        AIC      BIC    logLik</span></span>
+<span><span class="co">##   95.88804 99.44953 -43.94402</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
+<span><span class="co">##           Estimate Std. Error  Lower  Upper</span></span>
+<span><span class="co">## parent_0     92.47     1.2820 89.720 95.220</span></span>
+<span><span class="co">## log_alpha    11.37        NaN    NaN    NaN</span></span>
+<span><span class="co">## log_beta     13.72        NaN    NaN    NaN</span></span>
+<span><span class="co">## sigma         2.78     0.4621  1.789  3.771</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parameter correlation:</span></span>
+<span><span class="co">##            parent_0 log_alpha log_beta     sigma</span></span>
+<span><span class="co">## parent_0  1.0000000       NaN      NaN 0.0005548</span></span>
+<span><span class="co">## log_alpha       NaN         1      NaN       NaN</span></span>
+<span><span class="co">## log_beta        NaN       NaN        1       NaN</span></span>
+<span><span class="co">## sigma     0.0005548       NaN      NaN 1.0000000</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Backtransformed parameters:</span></span>
+<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
+<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
+<span><span class="co">## for estimators of untransformed parameters.</span></span>
+<span><span class="co">##           Estimate t value Pr(&gt;t)  Lower  Upper</span></span>
+<span><span class="co">## parent_0     92.47      NA     NA 89.720 95.220</span></span>
+<span><span class="co">## alpha     87110.00      NA     NA     NA     NA</span></span>
+<span><span class="co">## beta     911100.00      NA     NA     NA     NA</span></span>
+<span><span class="co">## sigma         2.78      NA     NA  1.789  3.771</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
+<span><span class="co">##          err.min n.optim df</span></span>
+<span><span class="co">## All data   3.619       3  6</span></span>
+<span><span class="co">## parent     3.619       3  6</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Estimated disappearance times:</span></span>
+<span><span class="co">##         DT50  DT90 DT50back</span></span>
+<span><span class="co">## parent 7.249 24.08    7.249</span></span></code></pre>
 <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p>
 <p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline">\(\chi^2\)</span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p>
 <p>The <span class="math inline">\(\chi^2\)</span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline">\(\chi^2\)</span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline">\(\chi^2\)</span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p>
@@ -318,21 +310,21 @@
 <h2 id="laboratory-data-l2">Laboratory Data L2<a class="anchor" aria-label="anchor" href="#laboratory-data-l2"></a>
 </h2>
 <p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">FOCUS_2006_L2</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>
-  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,
-  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,
-             <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,
-             <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span><span class="op">)</span><span class="op">)</span>
-<span class="va">FOCUS_2006_L2_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L2</span><span class="op">)</span></code></pre></div>
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">FOCUS_2006_L2</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
+<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span>
+<span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span>
+<span>             <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span>
+<span>             <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">FOCUS_2006_L2_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L2</span><span class="op">)</span></span></code></pre></div>
 <div class="section level3">
 <h3 id="sfo-fit-for-l2">SFO fit for L2<a class="anchor" aria-label="anchor" href="#sfo-fit-for-l2"></a>
 </h3>
 <p>Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument <code>show_residuals</code> to the plot command.</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">m.L2.SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>
-<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,
-     main <span class="op">=</span> <span class="st">"FOCUS L2 - SFO"</span><span class="op">)</span></code></pre></div>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">m.L2.SFO</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - SFO"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-8-1.png" width="672"></p>
 <p>The <span class="math inline">\(\chi^2\)</span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.</p>
 <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p>
@@ -342,169 +334,169 @@
 <h3 id="fomc-fit-for-l2">FOMC fit for L2<a class="anchor" aria-label="anchor" href="#fomc-fit-for-l2"></a>
 </h3>
 <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">m.L2.FOMC</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>,
-     main <span class="op">=</span> <span class="st">"FOCUS L2 - FOMC"</span><span class="op">)</span></code></pre></div>
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">m.L2.FOMC</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - FOMC"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
-<pre><code><span class="co">## mkin version used for fitting:    1.1.0 </span>
-<span class="co">## R version used for fitting:       4.2.1 </span>
-<span class="co">## Date of fit:     Thu Jun 30 10:44:01 2022 </span>
-<span class="co">## Date of summary: Thu Jun 30 10:44:01 2022 </span>
-<span class="co">## </span>
-<span class="co">## Equations:</span>
-<span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
-<span class="co">## </span>
-<span class="co">## Model predictions using solution type analytical </span>
-<span class="co">## </span>
-<span class="co">## Fitted using 239 model solutions performed in 0.049 s</span>
-<span class="co">## </span>
-<span class="co">## Error model: Constant variance </span>
-<span class="co">## </span>
-<span class="co">## Error model algorithm: OLS </span>
-<span class="co">## </span>
-<span class="co">## Starting values for parameters to be optimised:</span>
-<span class="co">##          value   type</span>
-<span class="co">## parent_0 93.95  state</span>
-<span class="co">## alpha     1.00 deparm</span>
-<span class="co">## beta     10.00 deparm</span>
-<span class="co">## </span>
-<span class="co">## Starting values for the transformed parameters actually optimised:</span>
-<span class="co">##               value lower upper</span>
-<span class="co">## parent_0  93.950000  -Inf   Inf</span>
-<span class="co">## log_alpha  0.000000  -Inf   Inf</span>
-<span class="co">## log_beta   2.302585  -Inf   Inf</span>
-<span class="co">## </span>
-<span class="co">## Fixed parameter values:</span>
-<span class="co">## None</span>
-<span class="co">## </span>
-<span class="co">## Results:</span>
-<span class="co">## </span>
-<span class="co">##        AIC      BIC    logLik</span>
-<span class="co">##   61.78966 63.72928 -26.89483</span>
-<span class="co">## </span>
-<span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="co">##           Estimate Std. Error    Lower   Upper</span>
-<span class="co">## parent_0   93.7700     1.6130 90.05000 97.4900</span>
-<span class="co">## log_alpha   0.3180     0.1559 -0.04149  0.6776</span>
-<span class="co">## log_beta    0.2102     0.2493 -0.36460  0.7850</span>
-<span class="co">## sigma       2.2760     0.4645  1.20500  3.3470</span>
-<span class="co">## </span>
-<span class="co">## Parameter correlation:</span>
-<span class="co">##             parent_0  log_alpha   log_beta      sigma</span>
-<span class="co">## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09</span>
-<span class="co">## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.602e-07</span>
-<span class="co">## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.372e-07</span>
-<span class="co">## sigma     -7.828e-09 -1.602e-07 -1.372e-07  1.000e+00</span>
-<span class="co">## </span>
-<span class="co">## Backtransformed parameters:</span>
-<span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="co">## for estimators of untransformed parameters.</span>
-<span class="co">##          Estimate t value    Pr(&gt;t)   Lower  Upper</span>
-<span class="co">## parent_0   93.770  58.120 4.267e-12 90.0500 97.490</span>
-<span class="co">## alpha       1.374   6.414 1.030e-04  0.9594  1.969</span>
-<span class="co">## beta        1.234   4.012 1.942e-03  0.6945  2.192</span>
-<span class="co">## sigma       2.276   4.899 5.977e-04  1.2050  3.347</span>
-<span class="co">## </span>
-<span class="co">## FOCUS Chi2 error levels in percent:</span>
-<span class="co">##          err.min n.optim df</span>
-<span class="co">## All data   6.205       3  3</span>
-<span class="co">## parent     6.205       3  3</span>
-<span class="co">## </span>
-<span class="co">## Estimated disappearance times:</span>
-<span class="co">##          DT50  DT90 DT50back</span>
-<span class="co">## parent 0.8092 5.356    1.612</span></code></pre>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.1.1 </span></span>
+<span><span class="co">## R version used for fitting:       4.2.1 </span></span>
+<span><span class="co">## Date of fit:     Fri Jul  8 17:34:01 2022 </span></span>
+<span><span class="co">## Date of summary: Fri Jul  8 17:34:01 2022 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Equations:</span></span>
+<span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model predictions using solution type analytical </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fitted using 239 model solutions performed in 0.044 s</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model: Constant variance </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model algorithm: OLS </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for parameters to be optimised:</span></span>
+<span><span class="co">##          value   type</span></span>
+<span><span class="co">## parent_0 93.95  state</span></span>
+<span><span class="co">## alpha     1.00 deparm</span></span>
+<span><span class="co">## beta     10.00 deparm</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
+<span><span class="co">##               value lower upper</span></span>
+<span><span class="co">## parent_0  93.950000  -Inf   Inf</span></span>
+<span><span class="co">## log_alpha  0.000000  -Inf   Inf</span></span>
+<span><span class="co">## log_beta   2.302585  -Inf   Inf</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fixed parameter values:</span></span>
+<span><span class="co">## None</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Results:</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##        AIC      BIC    logLik</span></span>
+<span><span class="co">##   61.78966 63.72928 -26.89483</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
+<span><span class="co">##           Estimate Std. Error    Lower   Upper</span></span>
+<span><span class="co">## parent_0   93.7700     1.6130 90.05000 97.4900</span></span>
+<span><span class="co">## log_alpha   0.3180     0.1559 -0.04149  0.6776</span></span>
+<span><span class="co">## log_beta    0.2102     0.2493 -0.36460  0.7850</span></span>
+<span><span class="co">## sigma       2.2760     0.4645  1.20500  3.3470</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parameter correlation:</span></span>
+<span><span class="co">##             parent_0  log_alpha   log_beta      sigma</span></span>
+<span><span class="co">## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.637e-09</span></span>
+<span><span class="co">## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.617e-07</span></span>
+<span><span class="co">## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.387e-07</span></span>
+<span><span class="co">## sigma     -7.637e-09 -1.617e-07 -1.387e-07  1.000e+00</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Backtransformed parameters:</span></span>
+<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
+<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
+<span><span class="co">## for estimators of untransformed parameters.</span></span>
+<span><span class="co">##          Estimate t value    Pr(&gt;t)   Lower  Upper</span></span>
+<span><span class="co">## parent_0   93.770  58.120 4.267e-12 90.0500 97.490</span></span>
+<span><span class="co">## alpha       1.374   6.414 1.030e-04  0.9594  1.969</span></span>
+<span><span class="co">## beta        1.234   4.012 1.942e-03  0.6945  2.192</span></span>
+<span><span class="co">## sigma       2.276   4.899 5.977e-04  1.2050  3.347</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
+<span><span class="co">##          err.min n.optim df</span></span>
+<span><span class="co">## All data   6.205       3  3</span></span>
+<span><span class="co">## parent     6.205       3  3</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Estimated disappearance times:</span></span>
+<span><span class="co">##          DT50  DT90 DT50back</span></span>
+<span><span class="co">## parent 0.8092 5.356    1.612</span></span></code></pre>
 <p>The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is much lower in this case. Therefore, the FOMC model provides a better description of the data, as less experimental error has to be assumed in order to explain the data.</p>
 </div>
 <div class="section level3">
 <h3 id="dfop-fit-for-l2">DFOP fit for L2<a class="anchor" aria-label="anchor" href="#dfop-fit-for-l2"></a>
 </h3>
 <p>Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level.</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">m.L2.DFOP</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,
-     main <span class="op">=</span> <span class="st">"FOCUS L2 - DFOP"</span><span class="op">)</span></code></pre></div>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">m.L2.DFOP</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>     main <span class="op">=</span> <span class="st">"FOCUS L2 - DFOP"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
-<pre><code><span class="co">## mkin version used for fitting:    1.1.0 </span>
-<span class="co">## R version used for fitting:       4.2.1 </span>
-<span class="co">## Date of fit:     Thu Jun 30 10:44:01 2022 </span>
-<span class="co">## Date of summary: Thu Jun 30 10:44:01 2022 </span>
-<span class="co">## </span>
-<span class="co">## Equations:</span>
-<span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="co">##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="co">##            * parent</span>
-<span class="co">## </span>
-<span class="co">## Model predictions using solution type analytical </span>
-<span class="co">## </span>
-<span class="co">## Fitted using 581 model solutions performed in 0.132 s</span>
-<span class="co">## </span>
-<span class="co">## Error model: Constant variance </span>
-<span class="co">## </span>
-<span class="co">## Error model algorithm: OLS </span>
-<span class="co">## </span>
-<span class="co">## Starting values for parameters to be optimised:</span>
-<span class="co">##          value   type</span>
-<span class="co">## parent_0 93.95  state</span>
-<span class="co">## k1        0.10 deparm</span>
-<span class="co">## k2        0.01 deparm</span>
-<span class="co">## g         0.50 deparm</span>
-<span class="co">## </span>
-<span class="co">## Starting values for the transformed parameters actually optimised:</span>
-<span class="co">##              value lower upper</span>
-<span class="co">## parent_0 93.950000  -Inf   Inf</span>
-<span class="co">## log_k1   -2.302585  -Inf   Inf</span>
-<span class="co">## log_k2   -4.605170  -Inf   Inf</span>
-<span class="co">## g_qlogis  0.000000  -Inf   Inf</span>
-<span class="co">## </span>
-<span class="co">## Fixed parameter values:</span>
-<span class="co">## None</span>
-<span class="co">## </span>
-<span class="co">## Results:</span>
-<span class="co">## </span>
-<span class="co">##        AIC      BIC    logLik</span>
-<span class="co">##   52.36695 54.79148 -21.18347</span>
-<span class="co">## </span>
-<span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="co">##          Estimate Std. Error      Lower     Upper</span>
-<span class="co">## parent_0   93.950  9.998e-01    91.5900   96.3100</span>
-<span class="co">## log_k1      3.112  1.842e+03 -4353.0000 4359.0000</span>
-<span class="co">## log_k2     -1.088  6.285e-02    -1.2370   -0.9394</span>
-<span class="co">## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638</span>
-<span class="co">## sigma       1.414  2.886e-01     0.7314    2.0960</span>
-<span class="co">## </span>
-<span class="co">## Parameter correlation:</span>
-<span class="co">##            parent_0     log_k1     log_k2   g_qlogis      sigma</span>
-<span class="co">## parent_0  1.000e+00  6.783e-07 -3.390e-10  2.665e-01 -2.967e-10</span>
-<span class="co">## log_k1    6.783e-07  1.000e+00  1.116e-04 -2.196e-04 -1.031e-05</span>
-<span class="co">## log_k2   -3.390e-10  1.116e-04  1.000e+00 -7.903e-01  2.917e-09</span>
-<span class="co">## g_qlogis  2.665e-01 -2.196e-04 -7.903e-01  1.000e+00 -4.408e-09</span>
-<span class="co">## sigma    -2.967e-10 -1.031e-05  2.917e-09 -4.408e-09  1.000e+00</span>
-<span class="co">## </span>
-<span class="co">## Backtransformed parameters:</span>
-<span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="co">## for estimators of untransformed parameters.</span>
-<span class="co">##          Estimate   t value    Pr(&gt;t)   Lower   Upper</span>
-<span class="co">## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100</span>
-<span class="co">## k1        22.4800 5.553e-04 4.998e-01  0.0000     Inf</span>
-<span class="co">## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909</span>
-<span class="co">## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591</span>
-<span class="co">## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960</span>
-<span class="co">## </span>
-<span class="co">## FOCUS Chi2 error levels in percent:</span>
-<span class="co">##          err.min n.optim df</span>
-<span class="co">## All data    2.53       4  2</span>
-<span class="co">## parent      2.53       4  2</span>
-<span class="co">## </span>
-<span class="co">## Estimated disappearance times:</span>
-<span class="co">##          DT50  DT90 DT50back DT50_k1 DT50_k2</span>
-<span class="co">## parent 0.5335 5.311    1.599 0.03084   2.058</span></code></pre>
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.1.1 </span></span>
+<span><span class="co">## R version used for fitting:       4.2.1 </span></span>
+<span><span class="co">## Date of fit:     Fri Jul  8 17:34:01 2022 </span></span>
+<span><span class="co">## Date of summary: Fri Jul  8 17:34:01 2022 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Equations:</span></span>
+<span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span>
+<span><span class="co">##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span></span>
+<span><span class="co">##            * parent</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model predictions using solution type analytical </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fitted using 581 model solutions performed in 0.119 s</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model: Constant variance </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model algorithm: OLS </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for parameters to be optimised:</span></span>
+<span><span class="co">##          value   type</span></span>
+<span><span class="co">## parent_0 93.95  state</span></span>
+<span><span class="co">## k1        0.10 deparm</span></span>
+<span><span class="co">## k2        0.01 deparm</span></span>
+<span><span class="co">## g         0.50 deparm</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
+<span><span class="co">##              value lower upper</span></span>
+<span><span class="co">## parent_0 93.950000  -Inf   Inf</span></span>
+<span><span class="co">## log_k1   -2.302585  -Inf   Inf</span></span>
+<span><span class="co">## log_k2   -4.605170  -Inf   Inf</span></span>
+<span><span class="co">## g_qlogis  0.000000  -Inf   Inf</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fixed parameter values:</span></span>
+<span><span class="co">## None</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Results:</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##        AIC      BIC    logLik</span></span>
+<span><span class="co">##   52.36695 54.79148 -21.18347</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
+<span><span class="co">##          Estimate Std. Error      Lower     Upper</span></span>
+<span><span class="co">## parent_0   93.950  9.998e-01    91.5900   96.3100</span></span>
+<span><span class="co">## log_k1      3.113  1.845e+03 -4360.0000 4367.0000</span></span>
+<span><span class="co">## log_k2     -1.088  6.285e-02    -1.2370   -0.9394</span></span>
+<span><span class="co">## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638</span></span>
+<span><span class="co">## sigma       1.414  2.886e-01     0.7314    2.0960</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parameter correlation:</span></span>
+<span><span class="co">##            parent_0     log_k1     log_k2   g_qlogis      sigma</span></span>
+<span><span class="co">## parent_0  1.000e+00  6.784e-07 -5.188e-10  2.665e-01 -5.800e-10</span></span>
+<span><span class="co">## log_k1    6.784e-07  1.000e+00  1.114e-04 -2.191e-04 -1.029e-05</span></span>
+<span><span class="co">## log_k2   -5.188e-10  1.114e-04  1.000e+00 -7.903e-01  5.080e-09</span></span>
+<span><span class="co">## g_qlogis  2.665e-01 -2.191e-04 -7.903e-01  1.000e+00 -7.991e-09</span></span>
+<span><span class="co">## sigma    -5.800e-10 -1.029e-05  5.080e-09 -7.991e-09  1.000e+00</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Backtransformed parameters:</span></span>
+<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
+<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
+<span><span class="co">## for estimators of untransformed parameters.</span></span>
+<span><span class="co">##          Estimate   t value    Pr(&gt;t)   Lower   Upper</span></span>
+<span><span class="co">## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100</span></span>
+<span><span class="co">## k1        22.4800 5.544e-04 4.998e-01  0.0000     Inf</span></span>
+<span><span class="co">## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909</span></span>
+<span><span class="co">## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591</span></span>
+<span><span class="co">## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
+<span><span class="co">##          err.min n.optim df</span></span>
+<span><span class="co">## All data    2.53       4  2</span></span>
+<span><span class="co">## parent      2.53       4  2</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Estimated disappearance times:</span></span>
+<span><span class="co">##          DT50  DT90 DT50back DT50_k1 DT50_k2</span></span>
+<span><span class="co">## parent 0.5335 5.311    1.599 0.03083   2.058</span></span></code></pre>
 <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion.</p>
 </div>
 </div>
@@ -512,20 +504,20 @@
 <h2 id="laboratory-data-l3">Laboratory Data L3<a class="anchor" aria-label="anchor" href="#laboratory-data-l3"></a>
 </h2>
 <p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">FOCUS_2006_L3</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>
-  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,
-  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">97.8</span>, <span class="fl">60</span>, <span class="fl">51</span>, <span class="fl">43</span>, <span class="fl">35</span>, <span class="fl">22</span>, <span class="fl">15</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span>
-<span class="va">FOCUS_2006_L3_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L3</span><span class="op">)</span></code></pre></div>
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">FOCUS_2006_L3</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
+<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span>
+<span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">97.8</span>, <span class="fl">60</span>, <span class="fl">51</span>, <span class="fl">43</span>, <span class="fl">35</span>, <span class="fl">22</span>, <span class="fl">15</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">FOCUS_2006_L3_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L3</span><span class="op">)</span></span></code></pre></div>
 <div class="section level3">
 <h3 id="fit-multiple-models">Fit multiple models<a class="anchor" aria-label="anchor" href="#fit-multiple-models"></a>
 </h3>
 <p>As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function <code>mmkin</code>. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="co"># Only use one core here, not to offend the CRAN checks</span>
-<span class="va">mm.L3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,
-               <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L3"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L3_mkin</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">)</span></code></pre></div>
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
+<span><span class="va">mm.L3</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>               <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L3"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L3_mkin</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-12-1.png" width="700"></p>
 <p>The <span class="math inline">\(\chi^2\)</span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the <span class="math inline">\(\chi^2\)</span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level considerably.</p>
 </div>
@@ -534,96 +526,96 @@
 </h3>
 <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p>
 <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p>
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.1.1 </span></span>
+<span><span class="co">## R version used for fitting:       4.2.1 </span></span>
+<span><span class="co">## Date of fit:     Fri Jul  8 17:34:02 2022 </span></span>
+<span><span class="co">## Date of summary: Fri Jul  8 17:34:02 2022 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Equations:</span></span>
+<span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span>
+<span><span class="co">##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span></span>
+<span><span class="co">##            * parent</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model predictions using solution type analytical </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fitted using 376 model solutions performed in 0.072 s</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model: Constant variance </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model algorithm: OLS </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for parameters to be optimised:</span></span>
+<span><span class="co">##          value   type</span></span>
+<span><span class="co">## parent_0 97.80  state</span></span>
+<span><span class="co">## k1        0.10 deparm</span></span>
+<span><span class="co">## k2        0.01 deparm</span></span>
+<span><span class="co">## g         0.50 deparm</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
+<span><span class="co">##              value lower upper</span></span>
+<span><span class="co">## parent_0 97.800000  -Inf   Inf</span></span>
+<span><span class="co">## log_k1   -2.302585  -Inf   Inf</span></span>
+<span><span class="co">## log_k2   -4.605170  -Inf   Inf</span></span>
+<span><span class="co">## g_qlogis  0.000000  -Inf   Inf</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fixed parameter values:</span></span>
+<span><span class="co">## None</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Results:</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##        AIC      BIC    logLik</span></span>
+<span><span class="co">##   32.97732 33.37453 -11.48866</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
+<span><span class="co">##          Estimate Std. Error   Lower      Upper</span></span>
+<span><span class="co">## parent_0  97.7500    1.01900 94.5000 101.000000</span></span>
+<span><span class="co">## log_k1    -0.6612    0.10050 -0.9812  -0.341300</span></span>
+<span><span class="co">## log_k2    -4.2860    0.04322 -4.4230  -4.148000</span></span>
+<span><span class="co">## g_qlogis  -0.1739    0.05270 -0.3416  -0.006142</span></span>
+<span><span class="co">## sigma      1.0170    0.25430  0.2079   1.827000</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parameter correlation:</span></span>
+<span><span class="co">##            parent_0     log_k1     log_k2   g_qlogis      sigma</span></span>
+<span><span class="co">## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.632e-08</span></span>
+<span><span class="co">## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.145e-07</span></span>
+<span><span class="co">## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.021e-06</span></span>
+<span><span class="co">## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.925e-07</span></span>
+<span><span class="co">## sigma    -9.632e-08  7.145e-07  1.021e-06 -7.925e-07  1.000e+00</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Backtransformed parameters:</span></span>
+<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
+<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
+<span><span class="co">## for estimators of untransformed parameters.</span></span>
+<span><span class="co">##          Estimate t value    Pr(&gt;t)    Lower     Upper</span></span>
+<span><span class="co">## parent_0 97.75000  95.960 1.248e-06 94.50000 101.00000</span></span>
+<span><span class="co">## k1        0.51620   9.947 1.081e-03  0.37490   0.71090</span></span>
+<span><span class="co">## k2        0.01376  23.140 8.840e-05  0.01199   0.01579</span></span>
+<span><span class="co">## g         0.45660  34.920 2.581e-05  0.41540   0.49850</span></span>
+<span><span class="co">## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
+<span><span class="co">##          err.min n.optim df</span></span>
+<span><span class="co">## All data   2.225       4  4</span></span>
+<span><span class="co">## parent     2.225       4  4</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Estimated disappearance times:</span></span>
+<span><span class="co">##         DT50 DT90 DT50back DT50_k1 DT50_k2</span></span>
+<span><span class="co">## parent 7.464  123    37.03   1.343   50.37</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Data:</span></span>
+<span><span class="co">##  time variable observed predicted residual</span></span>
+<span><span class="co">##     0   parent     97.8     97.75  0.05396</span></span>
+<span><span class="co">##     3   parent     60.0     60.45 -0.44933</span></span>
+<span><span class="co">##     7   parent     51.0     49.44  1.56338</span></span>
+<span><span class="co">##    14   parent     43.0     43.84 -0.83632</span></span>
+<span><span class="co">##    30   parent     35.0     35.15 -0.14707</span></span>
+<span><span class="co">##    60   parent     22.0     23.26 -1.25919</span></span>
+<span><span class="co">##    91   parent     15.0     15.18 -0.18181</span></span>
+<span><span class="co">##   120   parent     12.0     10.19  1.81395</span></span></code></pre>
 <div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div>
-<pre><code><span class="co">## mkin version used for fitting:    1.1.0 </span>
-<span class="co">## R version used for fitting:       4.2.1 </span>
-<span class="co">## Date of fit:     Thu Jun 30 10:44:02 2022 </span>
-<span class="co">## Date of summary: Thu Jun 30 10:44:02 2022 </span>
-<span class="co">## </span>
-<span class="co">## Equations:</span>
-<span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
-<span class="co">##            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
-<span class="co">##            * parent</span>
-<span class="co">## </span>
-<span class="co">## Model predictions using solution type analytical </span>
-<span class="co">## </span>
-<span class="co">## Fitted using 376 model solutions performed in 0.08 s</span>
-<span class="co">## </span>
-<span class="co">## Error model: Constant variance </span>
-<span class="co">## </span>
-<span class="co">## Error model algorithm: OLS </span>
-<span class="co">## </span>
-<span class="co">## Starting values for parameters to be optimised:</span>
-<span class="co">##          value   type</span>
-<span class="co">## parent_0 97.80  state</span>
-<span class="co">## k1        0.10 deparm</span>
-<span class="co">## k2        0.01 deparm</span>
-<span class="co">## g         0.50 deparm</span>
-<span class="co">## </span>
-<span class="co">## Starting values for the transformed parameters actually optimised:</span>
-<span class="co">##              value lower upper</span>
-<span class="co">## parent_0 97.800000  -Inf   Inf</span>
-<span class="co">## log_k1   -2.302585  -Inf   Inf</span>
-<span class="co">## log_k2   -4.605170  -Inf   Inf</span>
-<span class="co">## g_qlogis  0.000000  -Inf   Inf</span>
-<span class="co">## </span>
-<span class="co">## Fixed parameter values:</span>
-<span class="co">## None</span>
-<span class="co">## </span>
-<span class="co">## Results:</span>
-<span class="co">## </span>
-<span class="co">##        AIC      BIC    logLik</span>
-<span class="co">##   32.97732 33.37453 -11.48866</span>
-<span class="co">## </span>
-<span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="co">##          Estimate Std. Error   Lower      Upper</span>
-<span class="co">## parent_0  97.7500    1.01900 94.5000 101.000000</span>
-<span class="co">## log_k1    -0.6612    0.10050 -0.9812  -0.341300</span>
-<span class="co">## log_k2    -4.2860    0.04322 -4.4230  -4.148000</span>
-<span class="co">## g_qlogis  -0.1739    0.05270 -0.3416  -0.006142</span>
-<span class="co">## sigma      1.0170    0.25430  0.2079   1.827000</span>
-<span class="co">## </span>
-<span class="co">## Parameter correlation:</span>
-<span class="co">##            parent_0     log_k1     log_k2   g_qlogis      sigma</span>
-<span class="co">## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.664e-08</span>
-<span class="co">## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.147e-07</span>
-<span class="co">## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06</span>
-<span class="co">## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.926e-07</span>
-<span class="co">## sigma    -9.664e-08  7.147e-07  1.022e-06 -7.926e-07  1.000e+00</span>
-<span class="co">## </span>
-<span class="co">## Backtransformed parameters:</span>
-<span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="co">## for estimators of untransformed parameters.</span>
-<span class="co">##          Estimate t value    Pr(&gt;t)    Lower     Upper</span>
-<span class="co">## parent_0 97.75000  95.960 1.248e-06 94.50000 101.00000</span>
-<span class="co">## k1        0.51620   9.947 1.081e-03  0.37490   0.71090</span>
-<span class="co">## k2        0.01376  23.140 8.840e-05  0.01199   0.01579</span>
-<span class="co">## g         0.45660  34.920 2.581e-05  0.41540   0.49850</span>
-<span class="co">## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700</span>
-<span class="co">## </span>
-<span class="co">## FOCUS Chi2 error levels in percent:</span>
-<span class="co">##          err.min n.optim df</span>
-<span class="co">## All data   2.225       4  4</span>
-<span class="co">## parent     2.225       4  4</span>
-<span class="co">## </span>
-<span class="co">## Estimated disappearance times:</span>
-<span class="co">##         DT50 DT90 DT50back DT50_k1 DT50_k2</span>
-<span class="co">## parent 7.464  123    37.03   1.343   50.37</span>
-<span class="co">## </span>
-<span class="co">## Data:</span>
-<span class="co">##  time variable observed predicted residual</span>
-<span class="co">##     0   parent     97.8     97.75  0.05396</span>
-<span class="co">##     3   parent     60.0     60.45 -0.44933</span>
-<span class="co">##     7   parent     51.0     49.44  1.56338</span>
-<span class="co">##    14   parent     43.0     43.84 -0.83632</span>
-<span class="co">##    30   parent     35.0     35.15 -0.14707</span>
-<span class="co">##    60   parent     22.0     23.26 -1.25919</span>
-<span class="co">##    91   parent     15.0     15.18 -0.18181</span>
-<span class="co">##   120   parent     12.0     10.19  1.81395</span></code></pre>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-13-1.png" width="700"></p>
 <p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the <span class="math inline">\(\chi^2\)</span> error level criterion for laboratory data L3.</p>
 <p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</p>
@@ -633,155 +625,155 @@
 <h2 id="laboratory-data-l4">Laboratory Data L4<a class="anchor" aria-label="anchor" href="#laboratory-data-l4"></a>
 </h2>
 <p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="va">FOCUS_2006_L4</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>
-  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,
-  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span><span class="op">)</span><span class="op">)</span>
-<span class="va">FOCUS_2006_L4_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L4</span><span class="op">)</span></code></pre></div>
+<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">FOCUS_2006_L4</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
+<span>  t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span>
+<span>  parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="va">FOCUS_2006_L4_mkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L4</span><span class="op">)</span></span></code></pre></div>
 <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="co"># Only use one core here, not to offend the CRAN checks</span>
-<span class="va">mm.L4</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,
-               <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L4"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L4_mkin</span><span class="op">)</span>,
-               quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
-<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">)</span></code></pre></div>
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
+<span><span class="va">mm.L4</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>               <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L4"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L4_mkin</span><span class="op">)</span>,</span>
+<span>               quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">)</span></span></code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="700"></p>
 <p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p>
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.1.1 </span></span>
+<span><span class="co">## R version used for fitting:       4.2.1 </span></span>
+<span><span class="co">## Date of fit:     Fri Jul  8 17:34:02 2022 </span></span>
+<span><span class="co">## Date of summary: Fri Jul  8 17:34:02 2022 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Equations:</span></span>
+<span><span class="co">## d_parent/dt = - k_parent * parent</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model predictions using solution type analytical </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fitted using 142 model solutions performed in 0.026 s</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model: Constant variance </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model algorithm: OLS </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for parameters to be optimised:</span></span>
+<span><span class="co">##          value   type</span></span>
+<span><span class="co">## parent_0  96.6  state</span></span>
+<span><span class="co">## k_parent   0.1 deparm</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
+<span><span class="co">##                  value lower upper</span></span>
+<span><span class="co">## parent_0     96.600000  -Inf   Inf</span></span>
+<span><span class="co">## log_k_parent -2.302585  -Inf   Inf</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fixed parameter values:</span></span>
+<span><span class="co">## None</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Results:</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##        AIC      BIC    logLik</span></span>
+<span><span class="co">##   47.12133 47.35966 -20.56067</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
+<span><span class="co">##              Estimate Std. Error  Lower   Upper</span></span>
+<span><span class="co">## parent_0       96.440    1.69900 92.070 100.800</span></span>
+<span><span class="co">## log_k_parent   -5.030    0.07059 -5.211  -4.848</span></span>
+<span><span class="co">## sigma           3.162    0.79050  1.130   5.194</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parameter correlation:</span></span>
+<span><span class="co">##               parent_0 log_k_parent     sigma</span></span>
+<span><span class="co">## parent_0     1.000e+00    5.938e-01 3.440e-07</span></span>
+<span><span class="co">## log_k_parent 5.938e-01    1.000e+00 5.885e-07</span></span>
+<span><span class="co">## sigma        3.440e-07    5.885e-07 1.000e+00</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Backtransformed parameters:</span></span>
+<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
+<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
+<span><span class="co">## for estimators of untransformed parameters.</span></span>
+<span><span class="co">##           Estimate t value    Pr(&gt;t)     Lower     Upper</span></span>
+<span><span class="co">## parent_0 96.440000   56.77 1.604e-08 92.070000 1.008e+02</span></span>
+<span><span class="co">## k_parent  0.006541   14.17 1.578e-05  0.005455 7.842e-03</span></span>
+<span><span class="co">## sigma     3.162000    4.00 5.162e-03  1.130000 5.194e+00</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
+<span><span class="co">##          err.min n.optim df</span></span>
+<span><span class="co">## All data   3.287       2  6</span></span>
+<span><span class="co">## parent     3.287       2  6</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Estimated disappearance times:</span></span>
+<span><span class="co">##        DT50 DT90</span></span>
+<span><span class="co">## parent  106  352</span></span></code></pre>
 <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
-<pre><code><span class="co">## mkin version used for fitting:    1.1.0 </span>
-<span class="co">## R version used for fitting:       4.2.1 </span>
-<span class="co">## Date of fit:     Thu Jun 30 10:44:02 2022 </span>
-<span class="co">## Date of summary: Thu Jun 30 10:44:02 2022 </span>
-<span class="co">## </span>
-<span class="co">## Equations:</span>
-<span class="co">## d_parent/dt = - k_parent * parent</span>
-<span class="co">## </span>
-<span class="co">## Model predictions using solution type analytical </span>
-<span class="co">## </span>
-<span class="co">## Fitted using 142 model solutions performed in 0.03 s</span>
-<span class="co">## </span>
-<span class="co">## Error model: Constant variance </span>
-<span class="co">## </span>
-<span class="co">## Error model algorithm: OLS </span>
-<span class="co">## </span>
-<span class="co">## Starting values for parameters to be optimised:</span>
-<span class="co">##          value   type</span>
-<span class="co">## parent_0  96.6  state</span>
-<span class="co">## k_parent   0.1 deparm</span>
-<span class="co">## </span>
-<span class="co">## Starting values for the transformed parameters actually optimised:</span>
-<span class="co">##                  value lower upper</span>
-<span class="co">## parent_0     96.600000  -Inf   Inf</span>
-<span class="co">## log_k_parent -2.302585  -Inf   Inf</span>
-<span class="co">## </span>
-<span class="co">## Fixed parameter values:</span>
-<span class="co">## None</span>
-<span class="co">## </span>
-<span class="co">## Results:</span>
-<span class="co">## </span>
-<span class="co">##        AIC      BIC    logLik</span>
-<span class="co">##   47.12133 47.35966 -20.56067</span>
-<span class="co">## </span>
-<span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="co">##              Estimate Std. Error  Lower   Upper</span>
-<span class="co">## parent_0       96.440    1.69900 92.070 100.800</span>
-<span class="co">## log_k_parent   -5.030    0.07059 -5.211  -4.848</span>
-<span class="co">## sigma           3.162    0.79050  1.130   5.194</span>
-<span class="co">## </span>
-<span class="co">## Parameter correlation:</span>
-<span class="co">##               parent_0 log_k_parent     sigma</span>
-<span class="co">## parent_0     1.000e+00    5.938e-01 3.387e-07</span>
-<span class="co">## log_k_parent 5.938e-01    1.000e+00 5.830e-07</span>
-<span class="co">## sigma        3.387e-07    5.830e-07 1.000e+00</span>
-<span class="co">## </span>
-<span class="co">## Backtransformed parameters:</span>
-<span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="co">## for estimators of untransformed parameters.</span>
-<span class="co">##           Estimate t value    Pr(&gt;t)     Lower     Upper</span>
-<span class="co">## parent_0 96.440000   56.77 1.604e-08 92.070000 1.008e+02</span>
-<span class="co">## k_parent  0.006541   14.17 1.578e-05  0.005455 7.842e-03</span>
-<span class="co">## sigma     3.162000    4.00 5.162e-03  1.130000 5.194e+00</span>
-<span class="co">## </span>
-<span class="co">## FOCUS Chi2 error levels in percent:</span>
-<span class="co">##          err.min n.optim df</span>
-<span class="co">## All data   3.287       2  6</span>
-<span class="co">## parent     3.287       2  6</span>
-<span class="co">## </span>
-<span class="co">## Estimated disappearance times:</span>
-<span class="co">##        DT50 DT90</span>
-<span class="co">## parent  106  352</span></code></pre>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
-<pre><code><span class="co">## mkin version used for fitting:    1.1.0 </span>
-<span class="co">## R version used for fitting:       4.2.1 </span>
-<span class="co">## Date of fit:     Thu Jun 30 10:44:02 2022 </span>
-<span class="co">## Date of summary: Thu Jun 30 10:44:03 2022 </span>
-<span class="co">## </span>
-<span class="co">## Equations:</span>
-<span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span>
-<span class="co">## </span>
-<span class="co">## Model predictions using solution type analytical </span>
-<span class="co">## </span>
-<span class="co">## Fitted using 224 model solutions performed in 0.045 s</span>
-<span class="co">## </span>
-<span class="co">## Error model: Constant variance </span>
-<span class="co">## </span>
-<span class="co">## Error model algorithm: OLS </span>
-<span class="co">## </span>
-<span class="co">## Starting values for parameters to be optimised:</span>
-<span class="co">##          value   type</span>
-<span class="co">## parent_0  96.6  state</span>
-<span class="co">## alpha      1.0 deparm</span>
-<span class="co">## beta      10.0 deparm</span>
-<span class="co">## </span>
-<span class="co">## Starting values for the transformed parameters actually optimised:</span>
-<span class="co">##               value lower upper</span>
-<span class="co">## parent_0  96.600000  -Inf   Inf</span>
-<span class="co">## log_alpha  0.000000  -Inf   Inf</span>
-<span class="co">## log_beta   2.302585  -Inf   Inf</span>
-<span class="co">## </span>
-<span class="co">## Fixed parameter values:</span>
-<span class="co">## None</span>
-<span class="co">## </span>
-<span class="co">## Results:</span>
-<span class="co">## </span>
-<span class="co">##        AIC      BIC    logLik</span>
-<span class="co">##   40.37255 40.69032 -16.18628</span>
-<span class="co">## </span>
-<span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span>
-<span class="co">##           Estimate Std. Error   Lower    Upper</span>
-<span class="co">## parent_0   99.1400     1.2670 95.6300 102.7000</span>
-<span class="co">## log_alpha  -0.3506     0.2616 -1.0770   0.3756</span>
-<span class="co">## log_beta    4.1740     0.3938  3.0810   5.2670</span>
-<span class="co">## sigma       1.8300     0.4575  0.5598   3.1000</span>
-<span class="co">## </span>
-<span class="co">## Parameter correlation:</span>
-<span class="co">##             parent_0  log_alpha   log_beta      sigma</span>
-<span class="co">## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07</span>
-<span class="co">## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.478e-08</span>
-<span class="co">## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.211e-08</span>
-<span class="co">## sigma     -2.468e-07  2.478e-08  5.211e-08  1.000e+00</span>
-<span class="co">## </span>
-<span class="co">## Backtransformed parameters:</span>
-<span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span>
-<span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span>
-<span class="co">## for estimators of untransformed parameters.</span>
-<span class="co">##          Estimate t value    Pr(&gt;t)   Lower   Upper</span>
-<span class="co">## parent_0  99.1400  78.250 7.993e-08 95.6300 102.700</span>
-<span class="co">## alpha      0.7042   3.823 9.365e-03  0.3407   1.456</span>
-<span class="co">## beta      64.9800   2.540 3.201e-02 21.7800 193.900</span>
-<span class="co">## sigma      1.8300   4.000 8.065e-03  0.5598   3.100</span>
-<span class="co">## </span>
-<span class="co">## FOCUS Chi2 error levels in percent:</span>
-<span class="co">##          err.min n.optim df</span>
-<span class="co">## All data   2.029       3  5</span>
-<span class="co">## parent     2.029       3  5</span>
-<span class="co">## </span>
-<span class="co">## Estimated disappearance times:</span>
-<span class="co">##         DT50 DT90 DT50back</span>
-<span class="co">## parent 108.9 1644    494.9</span></code></pre>
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## mkin version used for fitting:    1.1.1 </span></span>
+<span><span class="co">## R version used for fitting:       4.2.1 </span></span>
+<span><span class="co">## Date of fit:     Fri Jul  8 17:34:02 2022 </span></span>
+<span><span class="co">## Date of summary: Fri Jul  8 17:34:02 2022 </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Equations:</span></span>
+<span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Model predictions using solution type analytical </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fitted using 224 model solutions performed in 0.041 s</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model: Constant variance </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Error model algorithm: OLS </span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for parameters to be optimised:</span></span>
+<span><span class="co">##          value   type</span></span>
+<span><span class="co">## parent_0  96.6  state</span></span>
+<span><span class="co">## alpha      1.0 deparm</span></span>
+<span><span class="co">## beta      10.0 deparm</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span>
+<span><span class="co">##               value lower upper</span></span>
+<span><span class="co">## parent_0  96.600000  -Inf   Inf</span></span>
+<span><span class="co">## log_alpha  0.000000  -Inf   Inf</span></span>
+<span><span class="co">## log_beta   2.302585  -Inf   Inf</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Fixed parameter values:</span></span>
+<span><span class="co">## None</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Results:</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">##        AIC      BIC    logLik</span></span>
+<span><span class="co">##   40.37255 40.69032 -16.18628</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span>
+<span><span class="co">##           Estimate Std. Error   Lower    Upper</span></span>
+<span><span class="co">## parent_0   99.1400     1.2670 95.6300 102.7000</span></span>
+<span><span class="co">## log_alpha  -0.3506     0.2616 -1.0770   0.3756</span></span>
+<span><span class="co">## log_beta    4.1740     0.3938  3.0810   5.2670</span></span>
+<span><span class="co">## sigma       1.8300     0.4575  0.5598   3.1000</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Parameter correlation:</span></span>
+<span><span class="co">##             parent_0  log_alpha   log_beta      sigma</span></span>
+<span><span class="co">## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.563e-07</span></span>
+<span><span class="co">## log_alpha -4.696e-01  1.000e+00  9.889e-01  4.066e-08</span></span>
+<span><span class="co">## log_beta  -5.543e-01  9.889e-01  1.000e+00  6.818e-08</span></span>
+<span><span class="co">## sigma     -2.563e-07  4.066e-08  6.818e-08  1.000e+00</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Backtransformed parameters:</span></span>
+<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span>
+<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span>
+<span><span class="co">## for estimators of untransformed parameters.</span></span>
+<span><span class="co">##          Estimate t value    Pr(&gt;t)   Lower   Upper</span></span>
+<span><span class="co">## parent_0  99.1400  78.250 7.993e-08 95.6300 102.700</span></span>
+<span><span class="co">## alpha      0.7042   3.823 9.365e-03  0.3407   1.456</span></span>
+<span><span class="co">## beta      64.9800   2.540 3.201e-02 21.7800 193.900</span></span>
+<span><span class="co">## sigma      1.8300   4.000 8.065e-03  0.5598   3.100</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span>
+<span><span class="co">##          err.min n.optim df</span></span>
+<span><span class="co">## All data   2.029       3  5</span></span>
+<span><span class="co">## parent     2.029       3  5</span></span>
+<span><span class="co">## </span></span>
+<span><span class="co">## Estimated disappearance times:</span></span>
+<span><span class="co">##         DT50 DT90 DT50back</span></span>
+<span><span class="co">## parent 108.9 1644    494.9</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
@@ -811,7 +803,7 @@
 
 <div class="pkgdown">
   <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p>
 </div>
 
       </footer>
diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png
index b6130527..b56e91e1 100644
--- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png
+++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 717c34a8..9cdfa9de 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -17,7 +17,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html
index 393d0218..2f7730bd 100644
--- a/docs/articles/web_only/benchmarks.html
+++ b/docs/articles/web_only/benchmarks.html
@@ -33,7 +33,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
@@ -105,7 +105,7 @@
       <h1 data-toc-skip>Benchmark timings for mkin</h1>
                         <h4 data-toc-skip class="author">Johannes Ranke</h4>
             
-            <h4 data-toc-skip class="date">Last change 1 July 2022 (rebuilt 2022-07-01)</h4>
+            <h4 data-toc-skip class="date">Last change 1 July 2022 (rebuilt 2022-07-08)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/benchmarks.rmd" class="external-link"><code>vignettes/web_only/benchmarks.rmd</code></a></small>
       <div class="hidden name"><code>benchmarks.rmd</code></div>
@@ -309,6 +309,14 @@
 <td align="right">1.877</td>
 <td align="right">3.906</td>
 </tr>
+<tr class="odd">
+<td align="left">Linux</td>
+<td align="left">i7-4710MQ</td>
+<td align="left">4.2.1</td>
+<td align="left">1.1.1</td>
+<td align="right">1.644</td>
+<td align="right">3.172</td>
+</tr>
 </tbody>
 </table>
 </div>
@@ -453,6 +461,15 @@
 <td align="right">8.058</td>
 <td align="right">3.339</td>
 </tr>
+<tr class="odd">
+<td align="left">Linux</td>
+<td align="left">i7-4710MQ</td>
+<td align="left">4.2.1</td>
+<td align="left">1.1.1</td>
+<td align="right">1.230</td>
+<td align="right">5.839</td>
+<td align="right">2.444</td>
+</tr>
 </tbody>
 </table>
 </div>
@@ -642,6 +659,18 @@
 <td align="right">2.302</td>
 <td align="right">3.463</td>
 </tr>
+<tr class="odd">
+<td align="left">Linux</td>
+<td align="left">i7-4710MQ</td>
+<td align="left">4.2.1</td>
+<td align="left">1.1.1</td>
+<td align="right">0.678</td>
+<td align="right">1.095</td>
+<td align="right">1.149</td>
+<td align="right">3.247</td>
+<td align="right">1.658</td>
+<td align="right">2.472</td>
+</tr>
 </tbody>
 </table>
 </div>
diff --git a/docs/articles/web_only/dimethenamid_2018.html b/docs/articles/web_only/dimethenamid_2018.html
index 25fd9f9e..b020a7b0 100644
--- a/docs/articles/web_only/dimethenamid_2018.html
+++ b/docs/articles/web_only/dimethenamid_2018.html
@@ -33,7 +33,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
@@ -105,7 +105,7 @@
       <h1 data-toc-skip>Example evaluations of the dimethenamid data from 2018</h1>
                         <h4 data-toc-skip class="author">Johannes Ranke</h4>
             
-            <h4 data-toc-skip class="date">Last change 1 July 2022, built on 01 Jul 2022</h4>
+            <h4 data-toc-skip class="date">Last change 1 July 2022, built on 08 Jul 2022</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/dimethenamid_2018.rmd" class="external-link"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small>
       <div class="hidden name"><code>dimethenamid_2018.rmd</code></div>
@@ -178,7 +178,7 @@ Status of individual fits:
 
       dataset
 model  Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot
-  DFOP OK    OK      C      OK      C       OK    
+  DFOP OK    OK      OK     OK      C       OK    
 
 OK: No warnings
 C: Optimisation did not converge:
@@ -286,21 +286,23 @@ DMTA_0    97.99583 96.50079 99.4909
 k1         0.06377  0.03432  0.0932
 k2         0.00848  0.00444  0.0125
 g          0.95701  0.91313  1.0009
-a.1        1.82141  1.65974  1.9831
-SD.DMTA_0  1.64787  0.45779  2.8379
+a.1        1.82141  1.60516  2.0377
+SD.DMTA_0  1.64787  0.45729  2.8384
 SD.k1      0.57439  0.24731  0.9015
-SD.k2      0.03296 -2.50143  2.5673
-SD.g       1.10266  0.32371  1.8816</code></pre>
+SD.k2      0.03296 -2.50524  2.5712
+SD.g       1.10266  0.32354  1.8818</code></pre>
 <p>While the other parameters converge to credible values, the variance of k2 (<code>omega2.k2</code>) converges to a very small value. The printout of the <code>saem.mmkin</code> model shows that the estimated standard deviation of k2 across the population of soils (<code>SD.k2</code>) is ill-defined, indicating overparameterisation of this model.</p>
 <p>When the DFOP model is fitted with the two-component error model, we also observe that the estimated variance of k2 becomes very small, while being ill-defined, as illustrated by the excessive confidence interval of <code>SD.k2</code>.</p>
 <div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
 <span>  control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span>
 <span><span class="va">f_parent_saemix_dfop_tc_moreiter</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span>
-<span>  control <span class="op">=</span> <span class="va">saemix_control_moreiter</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div>
+<span>  control <span class="op">=</span> <span class="va">saemix_control_moreiter</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div>
+<pre><code>Likelihood cannot be computed by Importance Sampling.</code></pre>
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png" width="700"></p>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">)</span></span></code></pre></div>
 <pre><code>Kinetic nonlinear mixed-effects model fit by SAEM
 Structural model:
@@ -316,21 +318,21 @@ Likelihood computed by importance sampling
   666 664   -323
 
 Fitted parameters:
-          estimate    lower    upper
-DMTA_0    98.27617  96.3088 100.2436
-k1         0.06437   0.0337   0.0950
-k2         0.00880   0.0063   0.0113
-g          0.95249   0.9100   0.9949
-a.1        1.06161   0.8625   1.2607
-b.1        0.02967   0.0226   0.0367
-SD.DMTA_0  2.06075   0.4187   3.7028
-SD.k1      0.59357   0.2561   0.9310
-SD.k2      0.00292 -10.2960  10.3019
-SD.g       1.05725   0.3808   1.7337</code></pre>
+          estimate     lower    upper
+DMTA_0    9.82e+01  96.27937 100.1783
+k1        6.41e-02   0.03333   0.0948
+k2        8.56e-03   0.00608   0.0110
+g         9.55e-01   0.91440   0.9947
+a.1       1.07e+00   0.86542   1.2647
+b.1       2.96e-02   0.02258   0.0367
+SD.DMTA_0 2.04e+00   0.40629   3.6678
+SD.k1     5.98e-01   0.25796   0.9373
+SD.k2     5.28e-04 -58.93251  58.9336
+SD.g      1.04e+00   0.36509   1.7083</code></pre>
 <p>Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29" class="external-link">bug to the saemix package</a>, as the algorithm does not converge in this case.</p>
 <p>An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin. When using this option, convergence is slower, but eventually the algorithm stops as well with the same error message.</p>
 <p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and the version with increased iterations can be compared using the model comparison function of the saemix package:</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">AIC_parent_saemix</span> <span class="op">&lt;-</span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span></span>
 <span>  <span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>,</span>
 <span>  <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>,</span>
@@ -338,7 +340,7 @@ SD.g       1.05725   0.3808   1.7337</code></pre>
 <span>  <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>,</span>
 <span>  <span class="va">f_parent_saemix_dfop_tc_moreiter</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span></code></pre></div>
 <pre><code>Likelihoods calculated by importance sampling</code></pre>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
 <span>  <span class="st">"SFO const"</span>, <span class="st">"SFO tc"</span>, <span class="st">"DFOP const"</span>, <span class="st">"DFOP tc"</span>, <span class="st">"DFOP tc more iterations"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span></span></code></pre></div>
@@ -346,10 +348,10 @@ SD.g       1.05725   0.3808   1.7337</code></pre>
 SFO const               796.38 795.34
 SFO tc                  798.38 797.13
 DFOP const              705.75 703.88
-DFOP tc                 665.65 663.57
-DFOP tc more iterations 665.88 663.80</code></pre>
+DFOP tc                 665.72 663.63
+DFOP tc more iterations    NaN    NaN</code></pre>
 <p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span> <span class="op">&lt;-</span></span>
 <span>  <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span>
 <span><span class="va">AIC_parent_saemix_methods</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
@@ -359,11 +361,11 @@ DFOP tc more iterations 665.88 663.80</code></pre>
 <span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods</span><span class="op">)</span></span></code></pre></div>
 <pre><code>    is     gq    lin 
-665.65 665.68 665.11 </code></pre>
+665.72 665.88 665.15 </code></pre>
 <p>The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value.</p>
 <p>In order to illustrate that the comparison of the three method depends on the degree of convergence obtained in the fit, the same comparison is shown below for the fit using the defaults for the number of iterations and the number of MCMC chains.</p>
 <p>When using OpenBlas for linear algebra, there is a large difference in the values obtained with Gaussian quadrature, so the larger number of iterations makes a lot of difference. When using the LAPACK version coming with Debian Bullseye, the AIC based on Gaussian quadrature is almost the same as the one obtained with the other methods, also when using defaults for the fit.</p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc_defaults</span> <span class="op">&lt;-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span>
 <span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span> <span class="op">&lt;-</span></span>
 <span>  <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span>
@@ -374,14 +376,14 @@ DFOP tc more iterations 665.88 663.80</code></pre>
 <span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods_defaults</span><span class="op">)</span></span></code></pre></div>
 <pre><code>    is     gq    lin 
-668.27 718.36 666.49 </code></pre>
+668.91 663.61 667.40 </code></pre>
 </div>
 </div>
 <div class="section level3">
 <h3 id="comparison">Comparison<a class="anchor" aria-label="anchor" href="#comparison"></a>
 </h3>
 <p>The following table gives the AIC values obtained with both backend packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).</p>
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">AIC_all</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
 <span>  check.names <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  <span class="st">"Degradation model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"SFO"</span>, <span class="st">"DFOP"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span>
@@ -406,7 +408,7 @@ DFOP tc more iterations 665.88 663.80</code></pre>
 <td align="left">SFO</td>
 <td align="left">const</td>
 <td align="right">796.60</td>
-<td align="right">796.60</td>
+<td align="right">794.17</td>
 <td align="right">796.38</td>
 </tr>
 <tr class="even">
@@ -420,15 +422,15 @@ DFOP tc more iterations 665.88 663.80</code></pre>
 <td align="left">DFOP</td>
 <td align="left">const</td>
 <td align="right">NA</td>
-<td align="right">671.98</td>
+<td align="right">704.95</td>
 <td align="right">705.75</td>
 </tr>
 <tr class="even">
 <td align="left">DFOP</td>
 <td align="left">tc</td>
 <td align="right">671.91</td>
-<td align="right">665.11</td>
-<td align="right">665.65</td>
+<td align="right">665.15</td>
+<td align="right">665.72</td>
 </tr>
 </tbody>
 </table>
@@ -443,15 +445,15 @@ DFOP tc more iterations 665.88 663.80</code></pre>
 <div class="section level2">
 <h2 id="session-info">Session Info<a class="anchor" aria-label="anchor" href="#session-info"></a>
 </h2>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <pre><code>R version 4.2.1 (2022-06-23)
 Platform: x86_64-pc-linux-gnu (64-bit)
 Running under: Debian GNU/Linux 11 (bullseye)
 
 Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.13.so
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
 
 locale:
  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
@@ -466,24 +468,24 @@ attached base packages:
 [8] base     
 
 other attached packages:
-[1] saemix_3.0   npde_3.2     nlme_3.1-158 mkin_1.1.0   knitr_1.39  
+[1] nlme_3.1-158 mkin_1.1.1   knitr_1.39  
 
 loaded via a namespace (and not attached):
  [1] deSolve_1.32      zoo_1.8-10        tidyselect_1.1.2  xfun_0.31        
  [5] bslib_0.3.1       purrr_0.3.4       lattice_0.20-45   colorspace_2.0-3 
- [9] vctrs_0.4.1       generics_0.1.2    htmltools_0.5.2   yaml_2.3.5       
-[13] utf8_1.2.2        rlang_1.0.3       pkgdown_2.0.5     jquerylib_0.1.4  
-[17] pillar_1.7.0      glue_1.6.2        DBI_1.1.3         lifecycle_1.0.1  
+ [9] vctrs_0.4.1       generics_0.1.3    htmltools_0.5.2   yaml_2.3.5       
+[13] utf8_1.2.2        rlang_1.0.3       pkgdown_2.0.5     saemix_3.0       
+[17] jquerylib_0.1.4   pillar_1.7.0      glue_1.6.2        lifecycle_1.0.1  
 [21] stringr_1.4.0     munsell_0.5.0     gtable_0.3.0      ragg_1.2.2       
-[25] codetools_0.2-18  memoise_2.0.1     evaluate_0.15     fastmap_1.1.0    
+[25] memoise_2.0.1     evaluate_0.15     npde_3.2          fastmap_1.1.0    
 [29] lmtest_0.9-40     fansi_1.0.3       highr_0.9         scales_1.2.0     
 [33] cachem_1.0.6      desc_1.4.1        jsonlite_1.8.0    systemfonts_1.0.4
-[37] fs_1.5.2          gridExtra_2.3     textshaping_0.3.6 ggplot2_3.3.6    
+[37] fs_1.5.2          textshaping_0.3.6 gridExtra_2.3     ggplot2_3.3.6    
 [41] digest_0.6.29     stringi_1.7.6     dplyr_1.0.9       grid_4.2.1       
 [45] rprojroot_2.0.3   cli_3.3.0         tools_4.2.1       magrittr_2.0.3   
 [49] sass_0.4.1        tibble_3.1.7      crayon_1.5.1      pkgconfig_2.0.3  
-[53] ellipsis_0.3.2    assertthat_0.2.1  rmarkdown_2.14    mclust_5.4.10    
-[57] R6_2.5.1          compiler_4.2.1   </code></pre>
+[53] ellipsis_0.3.2    rmarkdown_2.14    R6_2.5.1          mclust_5.4.10    
+[57] compiler_4.2.1   </code></pre>
 </div>
 <div class="section level2">
 <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
diff --git a/docs/authors.html b/docs/authors.html
index 998e6d54..afa0d11c 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -17,7 +17,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
@@ -109,13 +109,13 @@
 
     <p>Ranke J (2022).
 <em>mkin: Kinetic Evaluation of Chemical Degradation Data</em>.
-R package version 1.1.0, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. 
+R package version 1.1.1, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. 
 </p>
     <pre>@Manual{,
   title = {mkin: Kinetic Evaluation of Chemical Degradation Data},
   author = {Johannes Ranke},
   year = {2022},
-  note = {R package version 1.1.0},
+  note = {R package version 1.1.1},
   url = {https://pkgdown.jrwb.de/mkin/},
 }</pre>
 
diff --git a/docs/index.html b/docs/index.html
index 47775538..6e6a90fa 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -44,7 +44,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
diff --git a/docs/news/index.html b/docs/news/index.html
index b8202e95..e7a7f571 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -17,7 +17,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
@@ -83,7 +83,8 @@
 
     <div class="section level2">
 <h2 class="page-header" data-toc-text="1.1.1" id="mkin-111">mkin 1.1.1<a class="anchor" aria-label="anchor" href="#mkin-111"></a></h2>
-<ul><li><p>‘vignettes/FOCUS_L.rmd’: Remove an outdated note referring to a failure to calculate the covariance matrix for DFOP with the L2 dataset. Since 0.9.45.5 the covariance matrix is available</p></li>
+<ul><li><p>’R/{mkinmod,mkinpredict}.R: Store DLL information in mkinmod objects and use that information in mkinpredict to avoid a performance regression brought by a bugfix in R 4.2.x. Thanks to Tomas Kalibera for his analysis of the problem on the r-package-devel list and his suggestion on how to fix it.</p></li>
+<li><p>‘vignettes/FOCUS_L.rmd’: Remove an outdated note referring to a failure to calculate the covariance matrix for DFOP with the L2 dataset. Since 0.9.45.5 the covariance matrix is available</p></li>
 <li><p>‘vignettes/web_only/benchmarks.rmd’: Add the first benchmark data using my laptop system, therefore add the CPU when showing the benchmark results.</p></li>
 <li><p>‘dimethenamid_2018’: Update example code to use saemix</p></li>
 <li><p>‘CAKE_export’: Check for validity of the map argument, updates</p></li>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index bf228fe1..19332d6b 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
   benchmarks: web_only/benchmarks.html
   compiled_models: web_only/compiled_models.html
   dimethenamid_2018: web_only/dimethenamid_2018.html
-last_built: 2022-07-01T11:17Z
+last_built: 2022-07-08T15:33Z
 urls:
   reference: https://pkgdown.jrwb.de/mkin/reference
   article: https://pkgdown.jrwb.de/mkin/articles
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 876207da..63e3de8f 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -17,7 +17,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span>
       </span>
     </div>
 
diff --git a/test.log b/test.log
index 44a93ac1..4e634f93 100644
--- a/test.log
+++ b/test.log
@@ -3,46 +3,46 @@ Loading required package: parallel
 ℹ Testing mkin
 ✔ | F W S  OK | Context
 ✔ |         5 | AIC calculation
-✔ |         5 | Analytical solutions for coupled models [3.5s]
+✔ |         5 | Analytical solutions for coupled models [2.9s]
 ✔ |         5 | Calculation of Akaike weights
 ✔ |         3 | Export dataset for reading into CAKE
-✔ |        12 | Confidence intervals and p-values [1.0s]
-✔ |     1  12 | Dimethenamid data from 2018 [31.6s]
+✔ |        12 | Confidence intervals and p-values [0.9s]
+✔ |     1  12 | Dimethenamid data from 2018 [32.4s]
 ────────────────────────────────────────────────────────────────────────────────
 Skip (test_dmta.R:98:3): Different backends get consistent results for SFO-SFO3+, dimethenamid data
 Reason: Fitting this ODE model with saemix takes about 15 minutes on my system
 ────────────────────────────────────────────────────────────────────────────────
-✔ |        14 | Error model fitting [4.7s]
+✔ |        14 | Error model fitting [6.6s]
 ✔ |         5 | Time step normalisation
 ✔ |         4 | Calculation of FOCUS chi2 error levels [0.5s]
-✔ |        14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8s]
+✔ |        14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.7s]
 ✔ |         4 | Test fitting the decline of metabolites from their maximum [0.3s]
 ✔ |         1 | Fitting the logistic model [0.2s]
-✔ |     1  12 | Nonlinear mixed-effects models [0.3s]
+✔ |     1  12 | Nonlinear mixed-effects models [0.2s]
 ────────────────────────────────────────────────────────────────────────────────
 Skip (test_mixed.R:68:3): saemix results are reproducible for biphasic fits
 Reason: Fitting with saemix takes around 10 minutes when using deSolve
 ────────────────────────────────────────────────────────────────────────────────
 ✔ |         3 | Test dataset classes mkinds and mkindsg
-✔ |        10 | Special cases of mkinfit calls [0.4s]
+✔ |        10 | Special cases of mkinfit calls [0.3s]
 ✔ |         3 | mkinfit features [0.6s]
 ✔ |         8 | mkinmod model generation and printing [0.2s]
 ✔ |         3 | Model predictions with mkinpredict [0.3s]
-✔ |        16 | Evaluations according to 2015 NAFTA guidance [1.4s]
-✔ |         9 | Nonlinear mixed-effects models with nlme [8.0s]
-✔ |        16 | Plotting [10.1s]
+✔ |        16 | Evaluations according to 2015 NAFTA guidance [1.3s]
+✔ |         9 | Nonlinear mixed-effects models with nlme [7.9s]
+✔ |        16 | Plotting [10.2s]
 ✔ |         4 | Residuals extracted from mkinfit models
-✔ |        25 | saemix parent models [170.4s]
-✔ |         2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.4s]
-✔ |         7 | Fitting the SFORB model [3.6s]
+✔ |        25 | saemix parent models [157.4s]
+✔ |         2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.3s]
+✔ |         7 | Fitting the SFORB model [3.2s]
 ✔ |         1 | Summaries of old mkinfit objects
 ✔ |         4 | Summary [0.1s]
-✔ |         4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2s]
-✔ |         9 | Hypothesis tests [8.1s]
-✔ |         4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2s]
+✔ |         4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [1.9s]
+✔ |         9 | Hypothesis tests [6.6s]
+✔ |         4 | Calculation of maximum time weighted average concentrations (TWAs) [1.8s]
 
 ══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 252.5 s
+Duration: 238.2 s
 
 ── Skipped tests  ──────────────────────────────────────────────────────────────
 • Fitting this ODE model with saemix takes about 15 minutes on my system (1)
diff --git a/tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg b/tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg
index 051b46b1..541bf36d 100644
--- a/tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg
+++ b/tests/testthat/_snaps/plot/mixed-model-fit-for-nlme-object.svg
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diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index 1b9d2e23..9f768b06 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -255,6 +255,7 @@ if (window.hljs) {
 
 
 
+
 <style type="text/css">
 .main-container {
   max-width: 940px;
@@ -276,6 +277,9 @@ button.code-folding-btn:focus {
 summary {
   display: list-item;
 }
+details > summary > p:only-child {
+  display: inline;
+}
 pre code {
   padding: 0;
 }
@@ -360,7 +364,7 @@ pre code {
 
 <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 31 January 2019 (rebuilt 2022-03-02)</h4>
+<h4 class="date">Last change 31 January 2019 (rebuilt 2022-07-08)</h4>
 
 </div>
 
@@ -435,9 +439,9 @@ print(FOCUS_2006_D)</code></pre>
 <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p>
 <pre class="r"><code>summary(fit)</code></pre>
 <pre><code>## mkin version used for fitting:    1.1.0 
-## R version used for fitting:       4.1.2 
-## Date of fit:     Wed Mar  2 16:28:06 2022 
-## Date of summary: Wed Mar  2 16:28:07 2022 
+## R version used for fitting:       4.2.1 
+## Date of fit:     Fri Jul  8 15:44:37 2022 
+## Date of summary: Fri Jul  8 15:44:38 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -445,7 +449,7 @@ print(FOCUS_2006_D)</code></pre>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 401 model solutions performed in 0.149 s
+## Fitted using 401 model solutions performed in 0.13 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -488,11 +492,11 @@ print(FOCUS_2006_D)</code></pre>
 ## 
 ## Parameter correlation:
 ##                   parent_0 log_k_parent   log_k_m1 f_parent_qlogis      sigma
-## parent_0         1.000e+00    5.174e-01 -1.688e-01      -5.471e-01 -1.172e-06
-## log_k_parent     5.174e-01    1.000e+00 -3.263e-01      -5.426e-01 -8.483e-07
-## log_k_m1        -1.688e-01   -3.263e-01  1.000e+00       7.478e-01  8.205e-07
-## f_parent_qlogis -5.471e-01   -5.426e-01  7.478e-01       1.000e+00  1.305e-06
-## sigma           -1.172e-06   -8.483e-07  8.205e-07       1.305e-06  1.000e+00
+## parent_0         1.000e+00    5.174e-01 -1.688e-01      -5.471e-01 -1.174e-06
+## log_k_parent     5.174e-01    1.000e+00 -3.263e-01      -5.426e-01 -8.492e-07
+## log_k_m1        -1.688e-01   -3.263e-01  1.000e+00       7.478e-01  8.220e-07
+## f_parent_qlogis -5.471e-01   -5.426e-01  7.478e-01       1.000e+00  1.307e-06
+## sigma           -1.174e-06   -8.492e-07  8.220e-07       1.307e-06  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 45d264b4..13d0d9eb 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -1517,7 +1517,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 17 November 2016 (rebuilt 2022-05-18)</h4>
+<h4 class="date">Last change 18 May 2022 (rebuilt 2022-07-08)</h4>
 
 </div>
 
@@ -1537,16 +1537,16 @@ FOCUS_2006_L1_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L1)</code></pre>
 <pre class="r"><code>m.L1.SFO &lt;- mkinfit(&quot;SFO&quot;, FOCUS_2006_L1_mkin, quiet = TRUE)
 summary(m.L1.SFO)</code></pre>
 <pre><code>## mkin version used for fitting:    1.1.0 
-## R version used for fitting:       4.2.0 
-## Date of fit:     Wed May 18 20:03:22 2022 
-## Date of summary: Wed May 18 20:03:22 2022 
+## R version used for fitting:       4.2.1 
+## Date of fit:     Fri Jul  8 15:44:39 2022 
+## Date of summary: Fri Jul  8 15:44:39 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 133 model solutions performed in 0.032 s
+## Fitted using 133 model solutions performed in 0.029 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1636,16 +1636,16 @@ plot(m.L1.FOMC, show_errmin = TRUE, main = &quot;FOCUS L1 - FOMC&quot;)</code></
 <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
 ## doubtful</code></pre>
 <pre><code>## mkin version used for fitting:    1.1.0 
-## R version used for fitting:       4.2.0 
-## Date of fit:     Wed May 18 20:03:22 2022 
-## Date of summary: Wed May 18 20:03:22 2022 
+## R version used for fitting:       4.2.1 
+## Date of fit:     Fri Jul  8 15:44:39 2022 
+## Date of summary: Fri Jul  8 15:44:40 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 357 model solutions performed in 0.07 s
+## Fitted using 357 model solutions performed in 0.071 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1736,16 +1736,16 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 <p><img 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
 <pre><code>## mkin version used for fitting:    1.1.0 
-## R version used for fitting:       4.2.0 
-## Date of fit:     Wed May 18 20:03:22 2022 
-## Date of summary: Wed May 18 20:03:22 2022 
+## R version used for fitting:       4.2.1 
+## Date of fit:     Fri Jul  8 15:44:40 2022 
+## Date of summary: Fri Jul  8 15:44:40 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 239 model solutions performed in 0.043 s
+## Fitted using 239 model solutions performed in 0.045 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1814,9 +1814,9 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 <p><img 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
 <pre><code>## mkin version used for fitting:    1.1.0 
-## R version used for fitting:       4.2.0 
-## Date of fit:     Wed May 18 20:03:23 2022 
-## Date of summary: Wed May 18 20:03:23 2022 
+## R version used for fitting:       4.2.1 
+## Date of fit:     Fri Jul  8 15:44:40 2022 
+## Date of summary: Fri Jul  8 15:44:40 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1825,7 +1825,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 581 model solutions performed in 0.118 s
+## Fitted using 581 model solutions performed in 0.119 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1914,9 +1914,9 @@ plot(mm.L3)</code></pre>
 <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p>
 <pre class="r"><code>summary(mm.L3[[&quot;DFOP&quot;, 1]])</code></pre>
 <pre><code>## mkin version used for fitting:    1.1.0 
-## R version used for fitting:       4.2.0 
-## Date of fit:     Wed May 18 20:03:23 2022 
-## Date of summary: Wed May 18 20:03:23 2022 
+## R version used for fitting:       4.2.1 
+## Date of fit:     Fri Jul  8 15:44:40 2022 
+## Date of summary: Fri Jul  8 15:44:41 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1925,7 +1925,7 @@ plot(mm.L3)</code></pre>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 376 model solutions performed in 0.073 s
+## Fitted using 376 model solutions performed in 0.072 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -2022,9 +2022,9 @@ plot(mm.L4)</code></pre>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p>
 <pre class="r"><code>summary(mm.L4[[&quot;SFO&quot;, 1]], data = FALSE)</code></pre>
 <pre><code>## mkin version used for fitting:    1.1.0 
-## R version used for fitting:       4.2.0 
-## Date of fit:     Wed May 18 20:03:23 2022 
-## Date of summary: Wed May 18 20:03:23 2022 
+## R version used for fitting:       4.2.1 
+## Date of fit:     Fri Jul  8 15:44:41 2022 
+## Date of summary: Fri Jul  8 15:44:41 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -2086,16 +2086,16 @@ plot(mm.L4)</code></pre>
 ## parent  106  352</code></pre>
 <pre class="r"><code>summary(mm.L4[[&quot;FOMC&quot;, 1]], data = FALSE)</code></pre>
 <pre><code>## mkin version used for fitting:    1.1.0 
-## R version used for fitting:       4.2.0 
-## Date of fit:     Wed May 18 20:03:23 2022 
-## Date of summary: Wed May 18 20:03:23 2022 
+## R version used for fitting:       4.2.1 
+## Date of fit:     Fri Jul  8 15:44:41 2022 
+## Date of summary: Fri Jul  8 15:44:41 2022 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 224 model solutions performed in 0.04 s
+## Fitted using 224 model solutions performed in 0.041 s
 ## 
 ## Error model: Constant variance 
 ## 
diff --git a/vignettes/web_only/benchmarks.R b/vignettes/web_only/benchmarks.R
deleted file mode 100644
index c9ba5be3..00000000
--- a/vignettes/web_only/benchmarks.R
+++ /dev/null
@@ -1,112 +0,0 @@
-## ---- include = FALSE---------------------------------------------------------
-library(knitr)
-opts_chunk$set(tidy = FALSE, cache = FALSE)
-library("mkin")
-
-## ----include = FALSE----------------------------------------------------------
-cpu_model <- benchmarkme::get_cpu()$model_name
-# Abbreviate CPU identifiers
-cpu_model <- gsub("AMD ", "", cpu_model)
-cpu_model <- gsub("Intel\\(R\\) Core\\(TM\\) ", "", cpu_model)
-cpu_model <- gsub(" Eight-Core Processor", "", cpu_model)
-cpu_model <- gsub(" CPU @ 2.50GHz", "", cpu_model)
-
-operating_system <- Sys.info()[["sysname"]]
-mkin_version <- as.character(packageVersion("mkin"))
-R_version <- paste0(R.version$major, ".", R.version$minor)
-system_string <- paste0(operating_system, ", ", cpu_model, ", mkin ", mkin_version, ", R ", R_version)
-
-benchmark_path = normalizePath("~/git/mkin/vignettes/web_only/mkin_benchmarks.rda")
-load(benchmark_path)
-
-# Used for reformatting the data on 2022-06-30
-# mkin_benchmarks[, "R"] <- NA
-# mkin_benchmarks <- mkin_benchmarks[c(2, 1, 15, 3, 4:14)]
-# mkin_benchmarks[, "CPU"] <- gsub("AMD.*", "Ryzen 7 1700", mkin_benchmarks[, "CPU"])
-# mkin_benchmarks[, "CPU"] <- gsub("Intel.*", "i7-4710MQ", mkin_benchmarks[, "CPU"])
-# rownames(mkin_benchmarks) <- gsub("AMD Ryzen 7 1700 Eight-Core Processor", "Ryzen 7 1700", rownames(mkin_benchmarks))
-# rownames(mkin_benchmarks) <- gsub("Intel\\(R\\) Core\\(TM\\) i7-4710MQ CPU @ 2.50GHz", "i7-4710MQ", rownames(mkin_benchmarks))
-# rownames(mkin_benchmarks) <- gsub(" version", "", rownames(mkin_benchmarks))
-
-mkin_benchmarks[system_string, c("CPU", "OS", "mkin", "R")] <-
-  c(cpu_model, operating_system, mkin_version, R_version)
-
-if (mkin_version > "0.9.48.1") {
-  mmkin_bench <- function(models, datasets, error_model = "const") {
-    mmkin(models, datasets, error_model = error_model, cores = 1, quiet = TRUE)
-  }
-} else {
-  mmkin_bench <- function(models, datasets, error_model = NULL) {
-    mmkin(models, datasets, reweight.method = error_model, cores = 1, quiet = TRUE)
-  }
-}
-
-## ----parent_only, warning = FALSE---------------------------------------------
-FOCUS_C <- FOCUS_2006_C
-FOCUS_D <- subset(FOCUS_2006_D, value != 0)
-parent_datasets <- list(FOCUS_C, FOCUS_D)
-
-t1 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), parent_datasets))[["elapsed"]]
-t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), parent_datasets,
-    error_model = "tc"))[["elapsed"]]
-
-## ----one_metabolite, message = FALSE------------------------------------------
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
-FOMC_SFO <- mkinmod(
-  parent = mkinsub("FOMC", "m1"),
-  m1 = mkinsub("SFO"))
-DFOP_SFO <- mkinmod(
-  parent = mkinsub("FOMC", "m1"),
-  m1 = mkinsub("SFO"))
-t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D)))[["elapsed"]]
-t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),
-    error_model = "tc"))[["elapsed"]]
-t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),
-    error_model = "obs"))[["elapsed"]]
-
-## ----two_metabolites, message = FALSE-----------------------------------------
-m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
-                           M1 = mkinsub("SFO", "M2"),
-                           M2 = mkinsub("SFO"),
-                           use_of_ff = "max", quiet = TRUE)
-
-m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
-                           M1 = mkinsub("SFO"),
-                           M2 = mkinsub("SFO"),
-                           use_of_ff = "max", quiet = TRUE)
-
-SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data
-
-DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
-
-t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))[["elapsed"]]
-t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))[["elapsed"]]
-
-t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a),
-    error_model = "tc"))[["elapsed"]]
-t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c),
-    error_model = "tc"))[["elapsed"]]
-
-t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a),
-    error_model = "obs"))[["elapsed"]]
-t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c),
-    error_model = "obs"))[["elapsed"]]
-
-## ----results------------------------------------------------------------------
-mkin_benchmarks[system_string, paste0("t", 1:11)] <-
-  c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
-save(mkin_benchmarks, file = benchmark_path)
-# Hide rownames from kable for results section
-rownames(mkin_benchmarks) <- NULL
-
-## ---- echo = FALSE------------------------------------------------------------
-kable(mkin_benchmarks[, c(1:4, 5:6)])
-
-## ---- echo = FALSE------------------------------------------------------------
-kable(mkin_benchmarks[, c(1:4, 7:9)])
-
-## ---- echo = FALSE------------------------------------------------------------
-kable(mkin_benchmarks[, c(1:4, 10:15)])
-
diff --git a/vignettes/web_only/benchmarks.html b/vignettes/web_only/benchmarks.html
index a127a7b4..9923a8fa 100644
--- a/vignettes/web_only/benchmarks.html
+++ b/vignettes/web_only/benchmarks.html
@@ -1599,7 +1599,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Benchmark timings for mkin</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 1 July 2022 (rebuilt 2022-07-01)</h4>
+<h4 class="date">Last change 1 July 2022 (rebuilt 2022-07-08)</h4>
 
 </div>
 
@@ -1775,8 +1775,8 @@ rownames(mkin_benchmarks) &lt;- NULL</code></pre>
 <td align="left">Ryzen 7 1700</td>
 <td align="left">4.2.1</td>
 <td align="left">1.1.0</td>
-<td align="right">1.807</td>
-<td align="right">3.488</td>
+<td align="right">1.842</td>
+<td align="right">3.453</td>
 </tr>
 <tr class="odd">
 <td align="left">Linux</td>
@@ -1794,6 +1794,14 @@ rownames(mkin_benchmarks) &lt;- NULL</code></pre>
 <td align="right">1.877</td>
 <td align="right">3.906</td>
 </tr>
+<tr class="odd">
+<td align="left">Linux</td>
+<td align="left">i7-4710MQ</td>
+<td align="left">4.2.1</td>
+<td align="left">1.1.1</td>
+<td align="right">1.592</td>
+<td align="right">3.101</td>
+</tr>
 </tbody>
 </table>
 </div>
@@ -1917,9 +1925,9 @@ rownames(mkin_benchmarks) &lt;- NULL</code></pre>
 <td align="left">Ryzen 7 1700</td>
 <td align="left">4.2.1</td>
 <td align="left">1.1.0</td>
-<td align="right">3.788</td>
-<td align="right">19.727</td>
-<td align="right">8.846</td>
+<td align="right">3.802</td>
+<td align="right">21.247</td>
+<td align="right">8.461</td>
 </tr>
 <tr class="odd">
 <td align="left">Linux</td>
@@ -1939,6 +1947,15 @@ rownames(mkin_benchmarks) &lt;- NULL</code></pre>
 <td align="right">8.058</td>
 <td align="right">3.339</td>
 </tr>
+<tr class="odd">
+<td align="left">Linux</td>
+<td align="left">i7-4710MQ</td>
+<td align="left">4.2.1</td>
+<td align="left">1.1.1</td>
+<td align="right">1.198</td>
+<td align="right">5.740</td>
+<td align="right">2.388</td>
+</tr>
 </tbody>
 </table>
 </div>
@@ -2098,12 +2115,12 @@ rownames(mkin_benchmarks) &lt;- NULL</code></pre>
 <td align="left">Ryzen 7 1700</td>
 <td align="left">4.2.1</td>
 <td align="left">1.1.0</td>
-<td align="right">2.828</td>
-<td align="right">4.286</td>
-<td align="right">5.475</td>
-<td align="right">11.054</td>
-<td align="right">6.704</td>
-<td align="right">9.085</td>
+<td align="right">3.018</td>
+<td align="right">4.165</td>
+<td align="right">5.036</td>
+<td align="right">10.844</td>
+<td align="right">6.623</td>
+<td align="right">9.722</td>
 </tr>
 <tr class="odd">
 <td align="left">Linux</td>
@@ -2129,6 +2146,18 @@ rownames(mkin_benchmarks) &lt;- NULL</code></pre>
 <td align="right">2.302</td>
 <td align="right">3.463</td>
 </tr>
+<tr class="odd">
+<td align="left">Linux</td>
+<td align="left">i7-4710MQ</td>
+<td align="left">4.2.1</td>
+<td align="left">1.1.1</td>
+<td align="right">0.652</td>
+<td align="right">1.089</td>
+<td align="right">1.149</td>
+<td align="right">3.205</td>
+<td align="right">1.635</td>
+<td align="right">2.444</td>
+</tr>
 </tbody>
 </table>
 </div>
diff --git a/vignettes/web_only/dimethenamid_2018.R b/vignettes/web_only/dimethenamid_2018.R
deleted file mode 100644
index 2554cd13..00000000
--- a/vignettes/web_only/dimethenamid_2018.R
+++ /dev/null
@@ -1,152 +0,0 @@
-## ---- include = FALSE---------------------------------------------------------
-require(knitr)
-require(mkin)
-require(nlme)
-options(digits = 5)
-opts_chunk$set(
-  comment = "",
-  tidy = FALSE,
-  cache = TRUE
-)
-
-## ----dimethenamid_data--------------------------------------------------------
-library(mkin, quietly = TRUE)
-dmta_ds <- lapply(1:7, function(i) {
-  ds_i <- dimethenamid_2018$ds[[i]]$data
-  ds_i[ds_i$name == "DMTAP", "name"] <-  "DMTA"
-  ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
-  ds_i
-})
-names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
-dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
-dmta_ds[["Elliot 1"]] <- NULL
-dmta_ds[["Elliot 2"]] <- NULL
-
-## ----f_parent_mkin------------------------------------------------------------
-f_parent_mkin_const <- mmkin(c("SFO", "DFOP"), dmta_ds,
-  error_model = "const", quiet = TRUE)
-f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds,
-  error_model = "tc", quiet = TRUE)
-
-## ----f_parent_mkin_sfo_const--------------------------------------------------
-plot(mixed(f_parent_mkin_const["SFO", ]))
-
-## ----f_parent_mkin_dfop_const-------------------------------------------------
-plot(mixed(f_parent_mkin_const["DFOP", ]))
-
-## ----f_parent_mkin_dfop_const_test--------------------------------------------
-plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)
-
-## ----f_parent_mkin_dfop_tc_test-----------------------------------------------
-plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)
-
-## ----f_parent_mkin_dfop_tc_print----------------------------------------------
-print(f_parent_mkin_tc["DFOP", ])
-
-## ----f_parent_nlme, warning = FALSE-------------------------------------------
-library(nlme)
-f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ])
-# f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ])
-f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ])
-f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ])
-
-## ----AIC_parent_nlme----------------------------------------------------------
-anova(
-  f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc
-)
-
-## ----f_parent_nlme_logchol, warning = FALSE, eval = FALSE---------------------
-#  f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ],
-#    random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1)))
-#  anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol)
-#  f_parent_nlme_sfo_tc_logchol <- nlme(f_parent_mkin_tc["SFO", ],
-#    random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1)))
-#  anova(f_parent_nlme_sfo_tc, f_parent_nlme_sfo_tc_logchol)
-#  f_parent_nlme_dfop_tc_logchol <- nlme(f_parent_mkin_const["DFOP", ],
-#    random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)))
-#  anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol)
-
-## ----plot_parent_nlme---------------------------------------------------------
-plot(f_parent_nlme_dfop_tc)
-
-## ----saemix_control, results='hide'-------------------------------------------
-library(saemix)
-saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,
-    print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)
-saemix_control_moreiter <- saemixControl(nbiter.saemix = c(1600, 300), nb.chains = 15,
-    print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)
-saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 300), nb.chains = 15,
-    print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)
-
-## ----f_parent_saemix_sfo_const, results = 'hide', dependson = "saemix_control"----
-f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
-  control = saemix_control, transformations = "saemix")
-plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")
-
-## ----f_parent_saemix_sfo_tc, results = 'hide', dependson = "saemix_control"----
-f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
-  control = saemix_control, transformations = "saemix")
-plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")
-
-## ----f_parent_saemix_dfop_const, results = 'show', dependson = "saemix_control"----
-f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
-  control = saemix_control, transformations = "saemix")
-plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")
-print(f_parent_saemix_dfop_const)
-
-## ----f_parent_saemix_dfop_tc, results = 'show', dependson = "saemix_control"----
-f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
-  control = saemix_control, transformations = "saemix")
-f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
-  control = saemix_control_moreiter, transformations = "saemix")
-plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence")
-print(f_parent_saemix_dfop_tc)
-
-## ----AIC_parent_saemix, cache = FALSE-----------------------------------------
-AIC_parent_saemix <- saemix::compare.saemix(
-  f_parent_saemix_sfo_const$so,
-  f_parent_saemix_sfo_tc$so,
-  f_parent_saemix_dfop_const$so,
-  f_parent_saemix_dfop_tc$so,
-  f_parent_saemix_dfop_tc_moreiter$so)
-rownames(AIC_parent_saemix) <- c(
-  "SFO const", "SFO tc", "DFOP const", "DFOP tc", "DFOP tc more iterations")
-print(AIC_parent_saemix)
-
-## ----AIC_parent_saemix_methods, cache = FALSE---------------------------------
-f_parent_saemix_dfop_tc$so <-
-  saemix::llgq.saemix(f_parent_saemix_dfop_tc$so)
-AIC_parent_saemix_methods <- c(
-  is = AIC(f_parent_saemix_dfop_tc$so, method = "is"),
-  gq = AIC(f_parent_saemix_dfop_tc$so, method = "gq"),
-  lin = AIC(f_parent_saemix_dfop_tc$so, method = "lin")
-)
-print(AIC_parent_saemix_methods)
-
-## ----AIC_parent_saemix_methods_defaults, cache = FALSE------------------------
-f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc["DFOP", ])
-f_parent_saemix_dfop_tc_defaults$so <-
-  saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so)
-AIC_parent_saemix_methods_defaults <- c(
-  is = AIC(f_parent_saemix_dfop_tc_defaults$so, method = "is"),
-  gq = AIC(f_parent_saemix_dfop_tc_defaults$so, method = "gq"),
-  lin = AIC(f_parent_saemix_dfop_tc_defaults$so, method = "lin")
-)
-print(AIC_parent_saemix_methods_defaults)
-
-## ----AIC_all, cache = FALSE---------------------------------------------------
-AIC_all <- data.frame(
-  check.names = FALSE,
-  "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"),
-  "Error model" = c("const", "tc", "const", "tc"),
-  nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)),
-  saemix_lin = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
-    f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "lin"),
-  saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
-    f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "is")
-)
-kable(AIC_all)
-
-## ----sessionInfo, cache = FALSE-----------------------------------------------
-sessionInfo()
-
diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda
index 18211bb9..3687ab8e 100644
--- a/vignettes/web_only/mkin_benchmarks.rda
+++ b/vignettes/web_only/mkin_benchmarks.rda
author	Johannes Ranke <jranke@uni-bremen.de>	2022-07-08 17:39:44 +0200
committer	Johannes Ranke <jranke@uni-bremen.de>	2022-07-08 17:39:44 +0200
commit	f35e0b3d3b9f41bee2f5cc357afcb69e3aadad15 (patch)
tree	675d90c517a0e8a32c7c3af8ef631a5c588503d4
parent	16a7ed9548b37fe3c68c993651226fdc2dda6402 (diff)