Merge branch 'master' of kolab:mkin

138 files changed, 1582 insertions, 2849 deletions

diff --git a/README.html b/README.html
index 4d0fd270..c9f86c6b 100644
--- a/README.html
+++ b/README.html
@@ -20,8 +20,8 @@
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 <style type="text/css">code{white-space: pre;}</style>
 <style type="text/css">
@@ -30,10 +30,12 @@
   }
 </style>
 <script type="text/javascript">
-if (window.hljs && document.readyState && document.readyState === "complete") {
-   window.setTimeout(function() {
-      hljs.initHighlighting();
-   }, 0);
+if (window.hljs) {
+  hljs.configure({languages: []});
+  hljs.initHighlightingOnLoad();
+  if (document.readyState && document.readyState === "complete") {
+    window.setTimeout(function() { hljs.initHighlighting(); }, 0);
+  }
 }
 </script>
 
@@ -134,7 +136,7 @@ $(document).ready(function () {
 </div>
 <div id="usage" class="section level2">
 <h2>Usage</h2>
-<p>For a start, have a look a the code examples provided for <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mkinfit.html"><code>plot.mkinfit</code></a> and <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mmkin.html"><code>plot.mmkin</code></a>, and at the package vignettes <a href="http://kinfit.r-forge.r-project.org/mkin_static/articles/FOCUS_L.html"><code>FOCUS L</code></a> and <a href="http://kinfit.r-forge.r-project.org/mkin_static/articles/FOCUS_D.html"><code>FOCUS D</code></a>.</p>
+<p>For a start, have a look a the code examples provided for <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html"><code>plot.mkinfit</code></a> and <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>, and at the package vignettes <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html"><code>FOCUS L</code></a> and <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html"><code>FOCUS D</code></a>.</p>
 </div>
 <div id="documentation" class="section level2">
 <h2>Documentation</h2>
@@ -143,17 +145,17 @@ $(document).ready(function () {
 <div id="features" class="section level2">
 <h2>Features</h2>
 <ul>
-<li>Highly flexible model specification using <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li>
-<li>As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li>
-<li>Model solution (forward modelling) in the function <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li>
-<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="http://kinfit.r-forge.r-project.org/mkin_static/articles/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li>
-<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric logration transformation that is now used for the formation fractions.</li>
+<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li>
+<li>As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li>
+<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li>
+<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li>
+<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric logration transformation that is now used for the formation fractions.</li>
 <li>A side effect of this is that when parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which can not occur in a single experiment with a single defined radiolabel position.</li>
 <li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li>
 <li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li>
 <li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li>
 <li>Iteratively reweighted least squares fitting is implemented in a similar way as in KinGUII and CAKE (see below). Simply add the argument <code>reweight.method = &quot;obs&quot;</code> to your call to <code>mkinfit</code> and a separate variance componenent for each of the observed variables will be optimised in a second stage after the primary optimisation algorithm has converged.</li>
-<li>Iterative reweighting is also possible using the two-component error model for analytical data of <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/sigma_rl.html">Rocke and Lorenzato</a> using the argument <code>reweight.method = &quot;tc&quot;</code>.</li>
+<li>Iterative reweighting is also possible using the two-component error model for analytical data of <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_rl.html">Rocke and Lorenzato</a> using the argument <code>reweight.method = &quot;tc&quot;</code>.</li>
 <li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.</li>
 </ul>
 </div>
diff --git a/README.md b/README.md
index 4ff631f6..26cdcafb 100644
--- a/README.md
+++ b/README.md
@@ -27,12 +27,12 @@ detailed guidance and helpful tools have been developed as detailed in
 ## Usage
 
 For a start, have a look a the code examples provided for
-[`plot.mkinfit`](http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mkinfit.html)
+[`plot.mkinfit`](https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html)
 and
-[`plot.mmkin`](http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mmkin.html), and
+[`plot.mmkin`](https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html), and
 at the package vignettes
-[`FOCUS L`](http://kinfit.r-forge.r-project.org/mkin_static/articles/FOCUS_L.html) and
-[`FOCUS D`](http://kinfit.r-forge.r-project.org/mkin_static/articles/FOCUS_D.html).
+[`FOCUS L`](https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html) and
+[`FOCUS D`](https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html).
 
 ## Documentation
 
@@ -44,28 +44,28 @@ and at [R-Forge](http://kinfit.r-forge.r-project.org/mkin_static/index.html).
 ## Features
 
 * Highly flexible model specification using
-  [`mkinmod`](http://kinfit.r-forge.r-project.org/mkin_static/reference/mkinmod.html),
+  [`mkinmod`](https://pkgdown.jrwb.de/mkin/reference/mkinmod.html),
   including equilibrium reactions and using the single first-order
   reversible binding (SFORB) model, which will automatically create
   two latent state variables for the observed variable.
 * As of version 0.9-39, fitting of several models to several datasets, optionally in
   parallel, is supported, see for example
-  [`plot.mmkin`](http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mmkin.html).
+  [`plot.mmkin`](https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html).
 * Model solution (forward modelling) in the function
-  [`mkinpredict`](http://kinfit.r-forge.r-project.org/mkin_static/reference/mkinpredict.html)
+  [`mkinpredict`](https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html)
   is performed either using the analytical solution for the case of
   parent only degradation, an eigenvalue based solution if only simple
   first-order (SFO) or SFORB kinetics are used in the model, or
   using a numeric solver from the `deSolve` package (default is `lsoda`).
 * If a C compiler is installed, the kinetic models are compiled from automatically
   generated C code, see
-  [vignette `compiled_models`](http://kinfit.r-forge.r-project.org/mkin_static/articles/compiled_models.html).
+  [vignette `compiled_models`](https://pkgdown.jrwb.de/mkin/articles/compiled_models.html).
   The autogeneration of C code was
   inspired by the [`ccSolve`](https://github.com/karlines/ccSolve) package. Thanks
   to Karline Soetaert for her work on that.
 * By default, kinetic rate constants and kinetic formation fractions are
   transformed internally using
-  [`transform_odeparms`](http://kinfit.r-forge.r-project.org/mkin_static/reference/transform_odeparms.html)
+  [`transform_odeparms`](https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html)
   so their estimators can more reasonably be expected to follow
   a normal distribution. This has the side effect that no constraints
   are needed in the optimisation. Thanks to René Lehmann for the nice
@@ -90,8 +90,8 @@ and at [R-Forge](http://kinfit.r-forge.r-project.org/mkin_static/index.html).
   componenent for each of the observed variables will be optimised
   in a second stage after the primary optimisation algorithm has converged.
 * Iterative reweighting is also possible using the two-component error model
-  for analytical data of 
-  [Rocke and Lorenzato](http://kinfit.r-forge.r-project.org/mkin_static/reference/sigma_rl.html)
+  for analytical data of
+  [Rocke and Lorenzato](https://pkgdown.jrwb.de/mkin/reference/sigma_rl.html)
   using the argument `reweight.method = "tc"`.
 * When a metabolite decline phase is not described well by SFO kinetics,
   SFORB kinetics can be used for the metabolite.
diff --git a/docs/articles/FOCUS_D.R b/docs/articles/FOCUS_D.R
deleted file mode 100644
index b831e14e..00000000
--- a/docs/articles/FOCUS_D.R
+++ /dev/null
@@ -1,24 +0,0 @@
-## ---- include = FALSE----------------------------------------------------
-library(knitr)
-opts_chunk$set(tidy = FALSE, cache = TRUE)
-
-## ----data----------------------------------------------------------------
-library("mkin", quietly = TRUE)
-print(FOCUS_2006_D)
-
-## ----model---------------------------------------------------------------
-SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
-print(SFO_SFO$diffs)
-
-## ----fit-----------------------------------------------------------------
-fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
-
-## ----plot, fig.height = 6, fig.width = 8---------------------------------
-plot_sep(fit, lpos = c("topright", "bottomright"))
-
-## ----plot_2, fig.height = 4, fig.width = 8-------------------------------
-mkinparplot(fit)
-
-## ------------------------------------------------------------------------
-summary(fit)
-
diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html
index d9dd8ad5..af04f755 100644
--- a/docs/articles/FOCUS_D.html
+++ b/docs/articles/FOCUS_D.html
@@ -8,8 +8,11 @@
 <title>Example evaluation of FOCUS Example Dataset D • mkin</title>
 <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
 <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
-<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
+<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Example Dataset D">
+<meta property="og:description" content="">
+<meta name="twitter:card" content="summary">
+<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]-->
@@ -77,7 +80,7 @@
       <h1>Example evaluation of FOCUS Example Dataset D</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-16</h4>
+            <h4 class="date">2018-03-01</h4>
           </div>
 
     
@@ -85,7 +88,7 @@
 <div class="contents">
 <p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look a the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"mkin"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>)
-<span class="kw">print</span>(FOCUS_2006_D)</code></pre></div>
+<span class="kw">print</span>(FOCUS_<span class="dv">2006</span>_D)</code></pre></div>
 <pre><code>##      name time  value
 ## 1  parent    0  99.46
 ## 2  parent    0 102.04
@@ -135,13 +138,13 @@
 <p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">parent =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"m1"</span>), <span class="dt">m1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(SFO_SFO$diffs)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(SFO_SFO<span class="op">$</span>diffs)</code></pre></div>
 <pre><code>##                                                       parent 
 ## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" 
 ##                                                           m1 
 ##             "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"</code></pre>
 <p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fit &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_2006_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fit &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</code></pre></div>
 <p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(fit, <span class="dt">lpos =</span> <span class="kw">c</span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>))</code></pre></div>
 <p><img src="FOCUS_D_files/figure-html/plot-1.png" width="768"></p>
@@ -150,10 +153,10 @@
 <p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p>
 <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(fit)</code></pre></div>
-<pre><code>## mkin version:    0.9.46.1 
-## R version:       3.4.1 
-## Date of fit:     Thu Sep 14 12:15:01 2017 
-## Date of summary: Thu Sep 14 12:15:02 2017 
+<pre><code>## mkin version:    0.9.46.3 
+## R version:       3.4.3 
+## Date of fit:     Thu Mar  1 14:17:55 2018 
+## Date of summary: Thu Mar  1 14:17:55 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
@@ -161,7 +164,7 @@
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted with method Port using 153 model solutions performed in 1.14 s
+## Fitted with method Port using 153 model solutions performed in 0.993 s
 ## 
 ## Weighting: none
 ## 
@@ -286,7 +289,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/articles/FOCUS_D_files/figure-html/plot-1.png
index 75c4c299..b4fa2ff4 100644
--- a/docs/articles/FOCUS_D_files/figure-html/plot-1.png
+++ b/docs/articles/FOCUS_D_files/figure-html/plot-1.png
diff --git a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
index 94e7e2b3..ba06ce31 100644
--- a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
+++ b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index 42ec2df1..5de06ad5 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -8,8 +8,11 @@
 <title>Example evaluation of FOCUS Laboratory Data L1 to L3 • mkin</title>
 <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
 <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
-<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
+<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Laboratory Data L1 to L3">
+<meta property="og:description" content="">
+<meta name="twitter:card" content="summary">
+<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]-->
@@ -77,7 +80,7 @@
       <h1>Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-16</h4>
+            <h4 class="date">2018-03-01</h4>
           </div>
 
     
@@ -88,27 +91,27 @@
 <a href="#laboratory-data-l1" class="anchor"></a>Laboratory Data L1</h1>
 <p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"mkin"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>)
-FOCUS_2006_L1 =<span class="st"> </span><span class="kw">data.frame</span>(
+FOCUS_<span class="dv">2006</span>_L1 =<span class="st"> </span><span class="kw">data.frame</span>(
   <span class="dt">t =</span> <span class="kw">rep</span>(<span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="dt">each =</span> <span class="dv">2</span>),
   <span class="dt">parent =</span> <span class="kw">c</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>))
-FOCUS_2006_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_L1)</code></pre></div>
+FOCUS_<span class="dv">2006</span>_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_L1)</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"><pre class="sourceCode r"><code class="sourceCode r">m.L1.SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_2006_L1_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L1.SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_<span class="dv">2006</span>_L1_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw">summary</span>(m.L1.SFO)</code></pre></div>
-<pre><code>## mkin version:    0.9.47.1 
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Tue Jan 16 06:11:06 2018 
-## Date of summary: Tue Jan 16 06:11:06 2018 
+## Date of fit:     Thu Mar  1 14:31:57 2018 
+## Date of summary: Thu Mar  1 14:31:57 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.245 s
+## Fitted with method Port using 37 model solutions performed in 0.24 s
 ## 
 ## Weighting: none
 ## 
@@ -185,28 +188,21 @@ FOCUS_2006_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/mkinresplot.html">mkinresplot</a></span>(m.L1.SFO, <span class="dt">ylab =</span> <span class="st">"Observed"</span>, <span class="dt">xlab =</span> <span class="st">"Time"</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"><pre class="sourceCode r"><code class="sourceCode r">m.L1.FOMC &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"FOMC"</span>, FOCUS_2006_L1_mkin, <span class="dt">quiet=</span><span class="ot">TRUE</span>)</code></pre></div>
-<pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge.
-## Convergence code is 1</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(m.L1.FOMC, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>, <span class="dt">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L1.FOMC &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"FOMC"</span>, FOCUS_<span class="dv">2006</span>_L1_mkin, <span class="dt">quiet=</span><span class="ot">TRUE</span>)
+<span class="kw">plot</span>(m.L1.FOMC, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>, <span class="dt">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-6-1.png" width="576"></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L1.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
-<pre><code>## mkin version:    0.9.47.1 
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Tue Jan 16 06:11:07 2018 
-## Date of summary: Tue Jan 16 06:11:07 2018 
-## 
-## 
-## Warning: Optimisation by method Port did not converge.
-## Convergence code is 1 
-## 
+## Date of fit:     Thu Mar  1 14:31:59 2018 
+## Date of summary: Thu Mar  1 14:32:00 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 155 model solutions performed in 0.424 s
+## Fitted with method Port using 611 model solutions performed in 1.375 s
 ## 
 ## Weighting: none
 ## 
@@ -226,16 +222,16 @@ FOCUS_2006_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 ## None
 ## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
-##           Estimate Std. Error   Lower  Upper
-## parent_0     92.47      1.449   89.38  95.56
-## log_alpha    11.35    435.800 -917.60 940.30
-## log_beta     13.70    435.800 -915.20 942.60
+##           Estimate Std. Error    Lower   Upper
+## parent_0     92.47      1.482    89.31   95.63
+## log_alpha    11.25    598.200 -1264.00 1286.00
+## log_beta     13.60    598.200 -1261.00 1289.00
 ## 
 ## Parameter correlation:
 ##           parent_0 log_alpha log_beta
-## parent_0    1.0000    0.2209   0.2208
-## log_alpha   0.2209    1.0000   1.0000
-## log_beta    0.2208    1.0000   1.0000
+## parent_0    1.0000   -0.3016  -0.3016
+## log_alpha  -0.3016    1.0000   1.0000
+## log_beta   -0.3016    1.0000   1.0000
 ## 
 ## Residual standard error: 3.045 on 15 degrees of freedom
 ## 
@@ -244,9 +240,9 @@ FOCUS_2006_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 ## t-test (unrealistically) based on the assumption of normal distribution
 ## for estimators of untransformed parameters.
 ##           Estimate  t value    Pr(&gt;t) Lower Upper
-## parent_0     92.47 63.33000 6.183e-20 89.38 95.56
-## alpha     85190.00  0.03367 4.868e-01  0.00   Inf
-## beta     891000.00  0.03367 4.868e-01  0.00   Inf
+## parent_0     92.47 64.45000 4.757e-20 89.31 95.63
+## alpha     76830.00  0.02852 4.888e-01  0.00   Inf
+## beta     803500.00  0.02852 4.888e-01  0.00   Inf
 ## 
 ## Chi2 error levels in percent:
 ##          err.min n.optim df
@@ -264,17 +260,17 @@ FOCUS_2006_L1_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <h1 class="hasAnchor">
 <a href="#laboratory-data-l2" class="anchor"></a>Laboratory Data L2</h1>
 <p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_2006_L2 =<span class="st"> </span><span class="kw">data.frame</span>(
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_<span class="dv">2006</span>_L2 =<span class="st"> </span><span class="kw">data.frame</span>(
   <span class="dt">t =</span> <span class="kw">rep</span>(<span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="dt">each =</span> <span class="dv">2</span>),
   <span class="dt">parent =</span> <span class="kw">c</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>))
-FOCUS_2006_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_L2)</code></pre></div>
+FOCUS_<span class="dv">2006</span>_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_L2)</code></pre></div>
 <div id="sfo-fit-for-l2" class="section level2">
 <h2 class="hasAnchor">
 <a href="#sfo-fit-for-l2" class="anchor"></a>SFO fit for L2</h2>
 <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"><pre class="sourceCode r"><code class="sourceCode r">m.L2.SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_2006_L2_mkin, <span class="dt">quiet=</span><span class="ot">TRUE</span>)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_<span class="dv">2006</span>_L2_mkin, <span class="dt">quiet=</span><span class="ot">TRUE</span>)
 <span class="kw">plot</span>(m.L2.SFO, <span class="dt">show_residuals =</span> <span class="ot">TRUE</span>, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>,
      <span class="dt">main =</span> <span class="st">"FOCUS L2 - SFO"</span>)</code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-8-1.png" width="672"></p>
@@ -286,22 +282,22 @@ FOCUS_2006_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <h2 class="hasAnchor">
 <a href="#fomc-fit-for-l2" class="anchor"></a>FOMC fit for L2</h2>
 <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"><pre class="sourceCode r"><code class="sourceCode r">m.L2.FOMC &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"FOMC"</span>, FOCUS_2006_L2_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.FOMC &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"FOMC"</span>, FOCUS_<span class="dv">2006</span>_L2_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw">plot</span>(m.L2.FOMC, <span class="dt">show_residuals =</span> <span class="ot">TRUE</span>,
      <span class="dt">main =</span> <span class="st">"FOCUS L2 - FOMC"</span>)</code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L2.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
-<pre><code>## mkin version:    0.9.47.1 
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Tue Jan 16 06:11:08 2018 
-## Date of summary: Tue Jan 16 06:11:08 2018 
+## Date of fit:     Thu Mar  1 14:32:00 2018 
+## Date of summary: Thu Mar  1 14:32:00 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 81 model solutions performed in 0.168 s
+## Fitted with method Port using 81 model solutions performed in 0.158 s
 ## 
 ## Weighting: none
 ## 
@@ -357,15 +353,15 @@ FOCUS_2006_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <h2 class="hasAnchor">
 <a href="#dfop-fit-for-l2" class="anchor"></a>DFOP fit for L2</h2>
 <p>Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.DFOP &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"DFOP"</span>, FOCUS_2006_L2_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.DFOP &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"DFOP"</span>, FOCUS_<span class="dv">2006</span>_L2_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw">plot</span>(m.L2.DFOP, <span class="dt">show_residuals =</span> <span class="ot">TRUE</span>, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>,
      <span class="dt">main =</span> <span class="st">"FOCUS L2 - DFOP"</span>)</code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L2.DFOP, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
-<pre><code>## mkin version:    0.9.47.1 
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Tue Jan 16 06:11:09 2018 
-## Date of summary: Tue Jan 16 06:11:09 2018 
+## Date of fit:     Thu Mar  1 14:32:01 2018 
+## Date of summary: Thu Mar  1 14:32:01 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -374,7 +370,7 @@ FOCUS_2006_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 336 model solutions performed in 0.774 s
+## Fitted with method Port using 336 model solutions performed in 0.708 s
 ## 
 ## Weighting: none
 ## 
@@ -433,17 +429,17 @@ FOCUS_2006_L2_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../re
 <h1 class="hasAnchor">
 <a href="#laboratory-data-l3" class="anchor"></a>Laboratory Data L3</h1>
 <p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_2006_L3 =<span class="st"> </span><span class="kw">data.frame</span>(
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_<span class="dv">2006</span>_L3 =<span class="st"> </span><span class="kw">data.frame</span>(
   <span class="dt">t =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),
   <span class="dt">parent =</span> <span class="kw">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))
-FOCUS_2006_L3_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_L3)</code></pre></div>
+FOCUS_<span class="dv">2006</span>_L3_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_L3)</code></pre></div>
 <div id="fit-multiple-models" class="section level2">
 <h2 class="hasAnchor">
 <a href="#fit-multiple-models" class="anchor"></a>Fit multiple models</h2>
 <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"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Only use one core here, not to offend the CRAN checks</span>
 mm.L3 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin.html">mmkin</a></span>(<span class="kw">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>), <span class="dt">cores =</span> <span class="dv">1</span>,
-               <span class="kw">list</span>(<span class="st">"FOCUS L3"</span> =<span class="st"> </span>FOCUS_2006_L3_mkin), <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+               <span class="kw">list</span>(<span class="st">"FOCUS L3"</span> =<span class="st"> </span>FOCUS_<span class="dv">2006</span>_L3_mkin), <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw">plot</span>(mm.L3)</code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-12-1.png" width="672"></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>
@@ -454,10 +450,10 @@ mm.L3 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 <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"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</code></pre></div>
-<pre><code>## mkin version:    0.9.47.1 
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Tue Jan 16 06:11:10 2018 
-## Date of summary: Tue Jan 16 06:11:10 2018 
+## Date of fit:     Thu Mar  1 14:32:02 2018 
+## Date of summary: Thu Mar  1 14:32:02 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -542,30 +538,30 @@ mm.L3 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 <h1 class="hasAnchor">
 <a href="#laboratory-data-l4" class="anchor"></a>Laboratory Data L4</h1>
 <p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_2006_L4 =<span class="st"> </span><span class="kw">data.frame</span>(
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_<span class="dv">2006</span>_L4 =<span class="st"> </span><span class="kw">data.frame</span>(
   <span class="dt">t =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),
   <span class="dt">parent =</span> <span class="kw">c</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>))
-FOCUS_2006_L4_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_L4)</code></pre></div>
+FOCUS_<span class="dv">2006</span>_L4_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_L4)</code></pre></div>
 <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Only use one core here, not to offend the CRAN checks</span>
 mm.L4 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin.html">mmkin</a></span>(<span class="kw">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>), <span class="dt">cores =</span> <span class="dv">1</span>,
-               <span class="kw">list</span>(<span class="st">"FOCUS L4"</span> =<span class="st"> </span>FOCUS_2006_L4_mkin),
+               <span class="kw">list</span>(<span class="st">"FOCUS L4"</span> =<span class="st"> </span>FOCUS_<span class="dv">2006</span>_L4_mkin),
                <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw">plot</span>(mm.L4)</code></pre></div>
 <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="672"></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"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
-<pre><code>## mkin version:    0.9.47.1 
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Tue Jan 16 06:11:10 2018 
-## Date of summary: Tue Jan 16 06:11:10 2018 
+## Date of fit:     Thu Mar  1 14:32:03 2018 
+## Date of summary: Thu Mar  1 14:32:03 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.094 s
+## Fitted with method Port using 46 model solutions performed in 0.089 s
 ## 
 ## Weighting: none
 ## 
@@ -615,17 +611,17 @@ mm.L4 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 ##        DT50 DT90
 ## parent  106  352</code></pre>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div>
-<pre><code>## mkin version:    0.9.47.1 
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Tue Jan 16 06:11:10 2018 
-## Date of summary: Tue Jan 16 06:11:10 2018 
+## Date of fit:     Thu Mar  1 14:32:03 2018 
+## Date of summary: Thu Mar  1 14:32:03 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 66 model solutions performed in 0.139 s
+## Fitted with method Port using 66 model solutions performed in 0.134 s
 ## 
 ## Weighting: none
 ## 
@@ -690,7 +686,8 @@ mm.L4 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
         <div id="tocnav">
-      <h2>Contents</h2>
+      <h2 class="hasAnchor">
+<a href="#tocnav" class="anchor"></a>Contents</h2>
       <ul class="nav nav-pills nav-stacked">
 <li><a href="#laboratory-data-l1">Laboratory Data L1</a></li>
       <li>
@@ -720,7 +717,7 @@ mm.L4 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mmkin
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
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+++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png
diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png
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diff --git a/docs/articles/FOCUS_Z.R b/docs/articles/FOCUS_Z.R
deleted file mode 100644
index 4d2dffca..00000000
--- a/docs/articles/FOCUS_Z.R
+++ /dev/null
@@ -1,115 +0,0 @@
-## ---- include = FALSE----------------------------------------------------
-require(knitr)
-options(digits = 5)
-opts_chunk$set(engine='R', tidy = FALSE)
-
-## ---- echo = TRUE, fig = TRUE, fig.width = 8, fig.height = 7-------------
-library(mkin, quietly = TRUE)
-LOD = 0.5
-FOCUS_2006_Z = data.frame(
-  t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21,
-        42, 61, 96, 124),
-  Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8,
-         2.9, 3.5, 5.3, 4.4, 1.2, 0.7),
-  Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1,
-         1.6, 0.6, 0.5 * LOD, NA, NA, NA),
-  Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5,
-         0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA),
-  Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5,
-         25.2, 17.2, 4.8, 4.5, 2.8, 4.4))
-
-FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)
-
-## ----FOCUS_2006_Z_fits_1, echo=TRUE, fig.height=6------------------------
-Z.2a <- mkinmod(Z0 = mkinsub("SFO", "Z1"),
-                Z1 = mkinsub("SFO"))
-m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.2a)
-summary(m.Z.2a, data = FALSE)$bpar
-
-## ----FOCUS_2006_Z_fits_2, echo=TRUE, fig.height=6------------------------
-Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"),
-                   Z1 = mkinsub("SFO"),
-                   use_of_ff = "max")
-
-m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.2a.ff)
-summary(m.Z.2a.ff, data = FALSE)$bpar
-
-## ----FOCUS_2006_Z_fits_3, echo=TRUE, fig.height=6------------------------
-Z.3 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
-               Z1 = mkinsub("SFO"), use_of_ff = "max")
-m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.3)
-summary(m.Z.3, data = FALSE)$bpar
-
-## ----FOCUS_2006_Z_fits_5, echo=TRUE, fig.height=7------------------------
-Z.5 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
-               Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-               Z2 = mkinsub("SFO"), use_of_ff = "max")
-m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.5)
-
-## ----FOCUS_2006_Z_fits_6, echo=TRUE, fig.height=8------------------------
-Z.FOCUS <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
-                   Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                   Z2 = mkinsub("SFO", "Z3"),
-                   Z3 = mkinsub("SFO"),
-                   use_of_ff = "max")
-m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin,
-                     parms.ini = m.Z.5$bparms.ode,
-                     quiet = TRUE)
-plot_sep(m.Z.FOCUS)
-summary(m.Z.FOCUS, data = FALSE)$bpar
-endpoints(m.Z.FOCUS)
-
-## ----FOCUS_2006_Z_fits_7, echo=TRUE, fig.height=8------------------------
-Z.mkin.1 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
-                    Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                    Z2 = mkinsub("SFO", "Z3"),
-                    Z3 = mkinsub("SFORB"))
-m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.mkin.1)
-summary(m.Z.mkin.1, data = FALSE)$cov.unscaled
-
-## ----FOCUS_2006_Z_fits_9, echo=TRUE, fig.height=8------------------------
-Z.mkin.3 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
-                    Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                    Z2 = mkinsub("SFO"))
-m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.mkin.3)
-
-## ----FOCUS_2006_Z_fits_10, echo=TRUE, fig.height=8-----------------------
-Z.mkin.4 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
-                    Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                    Z2 = mkinsub("SFO", "Z3"),
-                    Z3 = mkinsub("SFO"))
-m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin,
-                      parms.ini = m.Z.mkin.3$bparms.ode,
-                      quiet = TRUE)
-plot_sep(m.Z.mkin.4)
-
-## ----FOCUS_2006_Z_fits_11, echo=TRUE, fig.height=8-----------------------
-Z.mkin.5 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
-                    Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                    Z2 = mkinsub("SFO", "Z3"),
-                    Z3 = mkinsub("SFORB"))
-m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,
-                      parms.ini = m.Z.mkin.4$bparms.ode[1:4],
-                      quiet = TRUE)
-plot_sep(m.Z.mkin.5)
-
-## ----FOCUS_2006_Z_fits_11a, echo=TRUE------------------------------------
-m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,
-                       parms.ini = c(m.Z.mkin.5$bparms.ode[1:7],
-                                     k_Z3_bound_free = 0),
-                       fixed_parms = "k_Z3_bound_free",
-                       quiet = TRUE)
-plot_sep(m.Z.mkin.5a)
-
-## ----FOCUS_2006_Z_fits_11b, echo=TRUE------------------------------------
-mkinparplot(m.Z.mkin.5a)
-
-## ----FOCUS_2006_Z_fits_11b_endpoints, echo=TRUE--------------------------
-endpoints(m.Z.mkin.5a)
-
diff --git a/docs/articles/FOCUS_Z.Rnw b/docs/articles/FOCUS_Z.Rnw
deleted file mode 100644
index 5abda0e1..00000000
--- a/docs/articles/FOCUS_Z.Rnw
+++ /dev/null
@@ -1,274 +0,0 @@
-%\VignetteIndexEntry{Example evaluation of FOCUS dataset Z}
-%\VignetteEngine{knitr::knitr}
-\documentclass[12pt,a4paper]{article}
-\usepackage{a4wide}
-\input{header}
-\hypersetup{
-  pdftitle = {Example evaluation of FOCUS dataset Z},
-  pdfsubject = {Manuscript},
-  pdfauthor = {Johannes Ranke},
-  colorlinks = {true},
-  linkcolor = {blue},
-  citecolor = {blue},
-  urlcolor = {red},
-  hyperindex = {true},
-  linktocpage = {true},
-}
-
-\begin{document}
-
-<<include=FALSE>>=
-require(knitr)
-opts_chunk$set(engine='R', tidy = FALSE, cache = TRUE)
-options(width=70)
-@
-
-\title{Example evaluation of FOCUS dataset Z}
-\author{\textbf{Johannes Ranke} \\[0.5cm]
-%EndAName
-Wissenschaftlicher Berater\\
-Kronacher Str. 8, 79639 Grenzach-Wyhlen, Germany\\[0.5cm]
-and\\[0.5cm]
-University of Bremen\\
-}
-\maketitle
-
-\thispagestyle{empty} \setcounter{page}{0}
-
-\clearpage
-
-\tableofcontents
-
-\textbf{Key words}: Kinetics, FOCUS, nonlinear optimisation
-
-\section{The data}
-
-The following code defines the example dataset from Appendix 7 to the FOCUS kinetics
-report \citep{FOCUSkinetics2011}, p.350.
-
-<<FOCUS_2006_Z_data, echo=TRUE, eval=TRUE>>=
-require(mkin)
-LOD = 0.5
-FOCUS_2006_Z = data.frame(
-  t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21,
-        42, 61, 96, 124),
-  Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8,
-         2.9, 3.5, 5.3, 4.4, 1.2, 0.7),
-  Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1,
-         1.6, 0.6, 0.5 * LOD, NA, NA, NA),
-  Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5,
-         0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA),
-  Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5,
-         25.2, 17.2, 4.8, 4.5, 2.8, 4.4))
-
-FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)
-@
-
-\section{Parent compound and one metabolite}
-
-The next step is to set up the models used for the kinetic analysis. As the
-simultaneous fit of parent and the first metabolite is usually straightforward,
-Step 1 (SFO for parent only) is skipped here. We start with the model 2a,
-with formation and decline of metabolite Z1 and the pathway from parent
-directly to sink included (default in mkin).
-
-<<FOCUS_2006_Z_fits_1, echo=TRUE, fig.height=6>>=
-Z.2a <- mkinmod(Z0 = mkinsub("SFO", "Z1"),
-                Z1 = mkinsub("SFO"))
-m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.2a)
-summary(m.Z.2a, data = FALSE)$bpar
-@
-
-As obvious from the parameter summary (the \texttt{bpar} component of the
-summary), the kinetic rate constant from parent compound Z to sink
-is negligible. Accordingly, the exact magnitude of the fitted parameter
-\texttt{log k\_Z0\_sink} is ill-defined and the covariance matrix is not
-returned (not shown, would be visible in the complete summary).
-This suggests, in agreement with the analysis in the FOCUS kinetics report, to
-simplify the model by removing the pathway to sink.
-
-A similar result can be obtained when formation fractions are used in the model
-formulation:
-
-<<FOCUS_2006_Z_fits_2, echo=TRUE, fig.height=6>>=
-Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"),
-                   Z1 = mkinsub("SFO"),
-                   use_of_ff = "max")
-
-m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.2a.ff)
-summary(m.Z.2a.ff, data = FALSE)$bpar
-@
-
-Here, the ilr transformed formation fraction fitted in the model takes a very
-large value, and the backtransformed formation fraction from parent Z to Z1 is
-practically unity. Again, the covariance matrix is not returned as the model is
-overparameterised.
-
-The simplified model is obtained by setting the list component \texttt{sink} to
-\texttt{FALSE}.\footnote{If the model formulation without formation fractions
-is used, the same effect can be obtained by fixing the parameter \texttt{k\_Z\_sink}
-to a value of zero.}
-
-In the following, we use the parameterisation with formation fractions in order
-to be able to compare with the results in the FOCUS guidance, and as it
-makes it easier to use parameters obtained in a previous fit when adding a further
-metabolite.
-
-<<FOCUS_2006_Z_fits_3, echo=TRUE, fig.height=6>>=
-Z.3 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
-               Z1 = mkinsub("SFO"), use_of_ff = "max")
-m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.3)
-summary(m.Z.3, data = FALSE)$bpar
-@
-
-As there is only one transformation product for Z0 and no pathway
-to sink, the formation fraction is internally fixed to unity.
-
-\section{Including metabolites Z2 and Z3}
-
-As suggested in the FOCUS report, the pathway to sink was removed for metabolite Z1 as
-well in the next step. While this step appears questionable on the basis of the above results, it
-is followed here for the purpose of comparison. Also, in the FOCUS report, it is
-assumed that there is additional empirical evidence that Z1 quickly and exclusively
-hydrolyses to Z2.
-
-<<FOCUS_2006_Z_fits_5, echo=TRUE, fig.height=7>>=
-Z.5 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
-               Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-               Z2 = mkinsub("SFO"), use_of_ff = "max")
-m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.5)
-@
-
-Finally, metabolite Z3 is added to the model. We use the optimised
-differential equation parameter values from the previous fit in order to
-accelerate the optimization.
-
-<<FOCUS_2006_Z_fits_6, echo=TRUE, fig.height=8>>=
-Z.FOCUS <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
-                   Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                   Z2 = mkinsub("SFO", "Z3"),
-                   Z3 = mkinsub("SFO"),
-                   use_of_ff = "max")
-m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin,
-                     parms.ini = m.Z.5$bparms.ode,
-                     quiet = TRUE)
-plot_sep(m.Z.FOCUS)
-summary(m.Z.FOCUS, data = FALSE)$bpar
-endpoints(m.Z.FOCUS)
-@
-
-This fit corresponds to the final result chosen in Appendix 7 of the FOCUS
-report. Confidence intervals returned by mkin are based on internally
-transformed parameters, however.
-
-\section{Using the SFORB model for parent and metabolites}
-
-As the FOCUS report states, there is a certain tailing of the time course of metabolite
-Z3. Also, the time course of the parent compound is not fitted very well using the
-SFO model, as residues at a certain low level remain.
-
-Therefore, an additional model is offered here, using the single first-order
-reversible binding (SFORB) model for metabolite Z3. As expected, the $\chi^2$
-error level is lower for metabolite Z3 using this model and the graphical
-fit for Z3 is improved. However, the covariance matrix is not returned.
-
-<<FOCUS_2006_Z_fits_7, echo=TRUE, fig.height=8>>=
-Z.mkin.1 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
-                    Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                    Z2 = mkinsub("SFO", "Z3"),
-                    Z3 = mkinsub("SFORB"))
-m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.mkin.1)
-summary(m.Z.mkin.1, data = FALSE)$cov.unscaled
-@
-
-Therefore, a further stepwise model building is performed starting from the
-stage of parent and two metabolites, starting from the assumption that the model
-fit for the parent compound can be improved by using the SFORB model.
-
-<<FOCUS_2006_Z_fits_9, echo=TRUE, fig.height=8>>=
-Z.mkin.3 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
-                    Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                    Z2 = mkinsub("SFO"))
-m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.mkin.3)
-@
-
-This results in a much better representation of the behaviour of the parent
-compound Z0.
-
-Finally, Z3 is added as well. These models appear overparameterised (no
-covariance matrix returned) if the sink for Z1 is left in the models.
-
-<<FOCUS_2006_Z_fits_10, echo=TRUE, fig.height=8>>=
-Z.mkin.4 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
-                    Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                    Z2 = mkinsub("SFO", "Z3"),
-                    Z3 = mkinsub("SFO"))
-m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin,
-                      parms.ini = m.Z.mkin.3$bparms.ode,
-                      quiet = TRUE)
-plot_sep(m.Z.mkin.4)
-@
-
-The error level of the fit, but especially of metabolite Z3, can be improved if
-the SFORB model is chosen for this metabolite, as this model is capable of
-representing the tailing of the metabolite decline phase.
-
-<<FOCUS_2006_Z_fits_11, echo=TRUE, fig.height=8>>=
-Z.mkin.5 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
-                    Z1 = mkinsub("SFO", "Z2", sink = FALSE),
-                    Z2 = mkinsub("SFO", "Z3"),
-                    Z3 = mkinsub("SFORB"))
-m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,
-                      parms.ini = m.Z.mkin.4$bparms.ode[1:4],
-                      quiet = TRUE)
-plot_sep(m.Z.mkin.5)
-@
-
-The summary view of the backtransformed parameters shows that we get no
-confidence intervals due to overparameterisation. As the optimized
-\texttt{k\_Z3\_bound\_free} is excessively small, it seems reasonable to fix it to
-zero.
-
-<<FOCUS_2006_Z_fits_11a, echo=TRUE>>=
-m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,
-                       parms.ini = c(m.Z.mkin.5$bparms.ode[1:7],
-                                     k_Z3_bound_free = 0),
-                       fixed_parms = "k_Z3_bound_free",
-                       quiet = TRUE)
-plot_sep(m.Z.mkin.5a)
-@
-
-As expected, the residual plots for Z0 and Z3 are more random than in the case of the
-all SFO model for which they were shown above. In conclusion, the model
-\texttt{Z.mkin.5a} is proposed as the best-fit model for the dataset from
-Appendix 7 of the FOCUS report.
-
-A graphical representation of the confidence intervals can finally be obtained.
-
-<<FOCUS_2006_Z_fits_11b, echo=TRUE>>=
-mkinparplot(m.Z.mkin.5a)
-@
-
-The endpoints obtained with this model are
-
-<<FOCUS_2006_Z_fits_11b_endpoints, echo=TRUE>>=
-endpoints(m.Z.mkin.5a)
-@
-
-It is clear the degradation rate of Z3 towards the end of the experiment
-is very low as DT50\_Z3\_b2 (the second Eigenvalue of the system of two differential
-equations representing the SFORB system for Z3, corresponding to the slower rate
-constant of the DFOP model) is reported to be infinity.  However, this appears
-to be a feature of the data.
-
-\bibliographystyle{plainnat}
-\bibliography{references}
-
-\end{document}
-% vim: set foldmethod=syntax:
diff --git a/docs/articles/FOCUS_Z.html b/docs/articles/FOCUS_Z.html
index a5cfc616..52a1db77 100644
--- a/docs/articles/FOCUS_Z.html
+++ b/docs/articles/FOCUS_Z.html
@@ -8,8 +8,11 @@
 <title>Example evaluation of FOCUS dataset Z • mkin</title>
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+<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS dataset Z">
+<meta property="og:description" content="">
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@@ -77,7 +80,7 @@
       <h1>Example evaluation of FOCUS dataset Z</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-16</h4>
+            <h4 class="date">2018-03-01</h4>
           </div>
 
     
@@ -90,64 +93,64 @@
 <p>The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 354)</span>.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(mkin, <span class="dt">quietly =</span> <span class="ot">TRUE</span>)
 LOD =<span class="st"> </span><span class="fl">0.5</span>
-FOCUS_2006_Z =<span class="st"> </span><span class="kw">data.frame</span>(
+FOCUS_<span class="dv">2006</span>_Z =<span class="st"> </span><span class="kw">data.frame</span>(
   <span class="dt">t =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.04</span>, <span class="fl">0.125</span>, <span class="fl">0.29</span>, <span class="fl">0.54</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">4</span>, <span class="dv">7</span>, <span class="dv">10</span>, <span class="dv">14</span>, <span class="dv">21</span>,
         <span class="dv">42</span>, <span class="dv">61</span>, <span class="dv">96</span>, <span class="dv">124</span>),
   <span class="dt">Z0 =</span> <span class="kw">c</span>(<span class="dv">100</span>, <span class="fl">81.7</span>, <span class="fl">70.4</span>, <span class="fl">51.1</span>, <span class="fl">41.2</span>, <span class="fl">6.6</span>, <span class="fl">4.6</span>, <span class="fl">3.9</span>, <span class="fl">4.6</span>, <span class="fl">4.3</span>, <span class="fl">6.8</span>,
          <span class="fl">2.9</span>, <span class="fl">3.5</span>, <span class="fl">5.3</span>, <span class="fl">4.4</span>, <span class="fl">1.2</span>, <span class="fl">0.7</span>),
   <span class="dt">Z1 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">18.3</span>, <span class="fl">29.6</span>, <span class="fl">46.3</span>, <span class="fl">55.1</span>, <span class="fl">65.7</span>, <span class="fl">39.1</span>, <span class="dv">36</span>, <span class="fl">15.3</span>, <span class="fl">5.6</span>, <span class="fl">1.1</span>,
-         <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> *<span class="st"> </span>LOD, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>),
-  <span class="dt">Z2 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="ot">NA</span>, <span class="fl">0.5</span> *<span class="st"> </span>LOD, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>,
-         <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> *<span class="st"> </span>LOD, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>),
-  <span class="dt">Z3 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="fl">0.5</span> *<span class="st"> </span>LOD, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>,
+         <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>LOD, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>),
+  <span class="dt">Z2 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="ot">NA</span>, <span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>LOD, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>,
+         <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>LOD, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>),
+  <span class="dt">Z3 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>LOD, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>,
          <span class="fl">25.2</span>, <span class="fl">17.2</span>, <span class="fl">4.8</span>, <span class="fl">4.5</span>, <span class="fl">2.8</span>, <span class="fl">4.4</span>))
 
-FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_Z)</code></pre></div>
+FOCUS_<span class="dv">2006</span>_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_Z)</code></pre></div>
 </div>
 <div id="parent-and-one-metabolite" class="section level1">
 <h1 class="hasAnchor">
 <a href="#parent-and-one-metabolite" class="anchor"></a>Parent and one metabolite</h1>
 <p>The next step is to set up the models used for the kinetic analysis. As the simultaneous fit of parent and the first metabolite is usually straightforward, Step 1 (SFO for parent only) is skipped here. We start with the model 2a, with formation and decline of metabolite Z1 and the pathway from parent directly to sink included (default in mkin).</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z.2a &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>),
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z<span class="fl">.2</span>a &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>),
                 <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.2a &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.2a, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
-<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.2a)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.2</span>a &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.2</span>a, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.2</span>a)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png" width="672"></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.2a, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div>
-<pre><code>##             Estimate se_notrans    t value     Pr(&gt;t)   Lower     Upper
-## Z0_0      9.7015e+01   3.553135 2.7304e+01 1.6792e-21 91.4014 102.62838
-## k_Z0_sink 6.2135e-10   0.226894 2.7385e-09 5.0000e-01  0.0000       Inf
-## k_Z0_Z1   2.2360e+00   0.165073 1.3546e+01 7.3939e-14  1.8374   2.72107
-## k_Z1_sink 4.8212e-01   0.065854 7.3212e+00 3.5520e-08  0.4006   0.58024</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.2</span>a, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>bpar</code></pre></div>
+<pre><code>##             Estimate se_notrans    t value     Pr(&gt;t) Lower Upper
+## Z0_0      9.7015e+01   3.553140 2.7304e+01 1.6793e-21    NA    NA
+## k_Z0_sink 1.2790e-11   0.226895 5.6368e-11 5.0000e-01    NA    NA
+## k_Z0_Z1   2.2360e+00   0.165073 1.3546e+01 7.3938e-14    NA    NA
+## k_Z1_sink 4.8212e-01   0.065854 7.3212e+00 3.5520e-08    NA    NA</code></pre>
 <p>As obvious from the parameter summary (the  component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p>
 <p>A similar result can be obtained when formation fractions are used in the model formulation:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z.2a.ff &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>),
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z<span class="fl">.2</span>a.ff &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>),
                    <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),
                    <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.2a.ff &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.2a.ff, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
-<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.2a.ff)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.2</span>a.ff &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.2</span>a.ff, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.2</span>a.ff)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png" width="672"></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.2a.ff, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.2</span>a.ff, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>bpar</code></pre></div>
 <pre><code>##            Estimate se_notrans t value     Pr(&gt;t) Lower Upper
-## Z0_0       97.01488   3.553146 27.3039 1.6793e-21    NA    NA
-## k_Z0        2.23601   0.216847 10.3114 3.6617e-11    NA    NA
+## Z0_0       97.01488   3.553145 27.3039 1.6793e-21    NA    NA
+## k_Z0        2.23601   0.216849 10.3114 3.6623e-11    NA    NA
 ## k_Z1        0.48212   0.065854  7.3211 3.5520e-08    NA    NA
-## f_Z0_to_Z1  1.00000   0.101473  9.8548 9.7071e-11    NA    NA</code></pre>
+## f_Z0_to_Z1  1.00000   0.101473  9.8548 9.7068e-11    NA    NA</code></pre>
 <p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p>
 <p>A simplified model is obtained by removing the pathway to the sink. </p>
 <p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z<span class="fl">.3</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>),
                <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.3</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.3</span>, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.3</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.3</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.3</span>)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png" width="672"></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.3</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.3</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>bpar</code></pre></div>
 <pre><code>##      Estimate se_notrans t value     Pr(&gt;t)    Lower   Upper
-## Z0_0 97.01488   2.681771  36.176 2.3636e-25 91.52152 102.508
-## k_Z0  2.23601   0.146862  15.225 2.2470e-15  1.95453   2.558
+## Z0_0 97.01488   2.681772  36.176 2.3636e-25 91.52152 102.508
+## k_Z0  2.23601   0.146861  15.225 2.2464e-15  1.95453   2.558
 ## k_Z1  0.48212   0.042687  11.294 3.0686e-12  0.40216   0.578</code></pre>
 <p>As there is only one transformation product for Z0 and no pathway to sink, the formation fraction is internally fixed to unity.</p>
 </div>
@@ -159,7 +162,7 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
                <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z2"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>),
                <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.5</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.5</span>, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.5</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.5</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.5</span>)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png" width="672"></p>
 <p>Finally, metabolite Z3 is added to the model. We use the optimised differential equation parameter values from the previous fit in order to accelerate the optimization.</p>
@@ -169,25 +172,25 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
                    <span class="dt">Z3 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),
                    <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.FOCUS &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.FOCUS, FOCUS_2006_Z_mkin,
-                     <span class="dt">parms.ini =</span> m.Z<span class="fl">.5</span>$bparms.ode,
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.FOCUS &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.FOCUS, FOCUS_<span class="dv">2006</span>_Z_mkin,
+                     <span class="dt">parms.ini =</span> m.Z<span class="fl">.5</span><span class="op">$</span>bparms.ode,
                      <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</code></pre></div>
 <pre><code>## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation by method Port did not converge.
 ## Convergence code is 1</code></pre>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.FOCUS)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="672"></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.FOCUS, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div>
-<pre><code>##            Estimate se_notrans t value     Pr(&gt;t)     Lower      Upper
-## Z0_0       96.84024   2.058814 47.0369 5.5723e-44 92.706852 100.973637
-## k_Z0        2.21540   0.118128 18.7543 7.7369e-25  1.990504   2.465708
-## k_Z1        0.47836   0.029294 16.3298 3.3443e-22  0.423035   0.540918
-## k_Z2        0.45166   0.044186 10.2218 3.0364e-14  0.371065   0.549767
-## k_Z3        0.05869   0.014290  4.1072 7.2560e-05  0.035983   0.095725
-## f_Z2_to_Z3  0.47147   0.057027  8.2676 2.7790e-11  0.360295   0.585556</code></pre>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.FOCUS, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>bpar</code></pre></div>
+<pre><code>##             Estimate se_notrans t value     Pr(&gt;t)     Lower      Upper
+## Z0_0       96.837112   2.058861 47.0343 5.5877e-44 92.703779 100.970445
+## k_Z0        2.215368   0.118098 18.7587 7.6563e-25  1.990525   2.465609
+## k_Z1        0.478302   0.029289 16.3302 3.3408e-22  0.422977   0.540864
+## k_Z2        0.451617   0.044214 10.2144 3.1133e-14  0.371034   0.549702
+## k_Z3        0.058693   0.014296  4.1056 7.2924e-05  0.035994   0.095705
+## f_Z2_to_Z3  0.471516   0.057057  8.2639 2.8156e-11  0.360381   0.585548</code></pre>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/endpoints.html">endpoints</a></span>(m.Z.FOCUS)</code></pre></div>
 <pre><code>## $ff
 ##   Z2_Z3 Z2_sink 
-## 0.47147 0.52853 
+## 0.47152 0.52848 
 ## 
 ## $SFORB
 ## logical(0)
@@ -195,9 +198,9 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
 ## $distimes
 ##        DT50    DT90
 ## Z0  0.31288  1.0394
-## Z1  1.44901  4.8135
-## Z2  1.53466  5.0980
-## Z3 11.81037 39.2332</code></pre>
+## Z1  1.44918  4.8141
+## Z2  1.53481  5.0985
+## Z3 11.80973 39.2311</code></pre>
 <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p>
 </div>
 <div id="using-the-sforb-model" class="section level1">
@@ -210,17 +213,17 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
                     <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z3"</span>),
                     <span class="dt">Z3 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>))</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.1</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.1</span>, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.1</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.1</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.1</span>)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png" width="672"></p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.mkin<span class="fl">.1</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)$cov.unscaled</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.mkin<span class="fl">.1</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>cov.unscaled</code></pre></div>
 <pre><code>## NULL</code></pre>
 <p>Therefore, a further stepwise model building is performed starting from the stage of parent and two metabolites, starting from the assumption that the model fit for the parent compound can be improved by using the SFORB model.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z.mkin<span class="fl">.3</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>),
                     <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z2"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>),
                     <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.3</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.3</span>, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.3</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.3</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.3</span>)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png" width="672"></p>
 <p>This results in a much better representation of the behaviour of the parent compound Z0.</p>
@@ -230,8 +233,8 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
                     <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z3"</span>),
                     <span class="dt">Z3 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.4</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.4</span>, FOCUS_2006_Z_mkin,
-                      <span class="dt">parms.ini =</span> m.Z.mkin<span class="fl">.3</span>$bparms.ode,
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.4</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.4</span>, FOCUS_<span class="dv">2006</span>_Z_mkin,
+                      <span class="dt">parms.ini =</span> m.Z.mkin<span class="fl">.3</span><span class="op">$</span>bparms.ode,
                       <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.4</span>)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png" width="672"></p>
@@ -241,36 +244,36 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
                     <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z3"</span>),
                     <span class="dt">Z3 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>))</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.5</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.5</span>, FOCUS_2006_Z_mkin,
-                      <span class="dt">parms.ini =</span> m.Z.mkin<span class="fl">.4</span>$bparms.ode[<span class="dv">1</span>:<span class="dv">4</span>],
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.5</span> &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.5</span>, FOCUS_<span class="dv">2006</span>_Z_mkin,
+                      <span class="dt">parms.ini =</span> m.Z.mkin<span class="fl">.4</span><span class="op">$</span>bparms.ode[<span class="dv">1</span><span class="op">:</span><span class="dv">4</span>],
                       <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
 <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.5</span>)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png" width="672"></p>
 <p>The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized  is excessively small, it seems reasonable to fix it to zero.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin.5a &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.5</span>, FOCUS_2006_Z_mkin,
-                       <span class="dt">parms.ini =</span> <span class="kw">c</span>(m.Z.mkin<span class="fl">.5</span>$bparms.ode[<span class="dv">1</span>:<span class="dv">7</span>],
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.5</span>a &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.5</span>, FOCUS_<span class="dv">2006</span>_Z_mkin,
+                       <span class="dt">parms.ini =</span> <span class="kw">c</span>(m.Z.mkin<span class="fl">.5</span><span class="op">$</span>bparms.ode[<span class="dv">1</span><span class="op">:</span><span class="dv">7</span>],
                                      <span class="dt">k_Z3_bound_free =</span> <span class="dv">0</span>),
                        <span class="dt">fixed_parms =</span> <span class="st">"k_Z3_bound_free"</span>,
                        <span class="dt">quiet =</span> <span class="ot">TRUE</span>)
-<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin.5a)</code></pre></div>
+<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.5</span>a)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png" width="672"></p>
 <p>As expected, the residual plots for Z0 and Z3 are more random than in the case of the all SFO model for which they were shown above. In conclusion, the model  is proposed as the best-fit model for the dataset from Appendix 7 of the FOCUS report.</p>
 <p>A graphical representation of the confidence intervals can finally be obtained.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/mkinparplot.html">mkinparplot</a></span>(m.Z.mkin.5a)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/mkinparplot.html">mkinparplot</a></span>(m.Z.mkin<span class="fl">.5</span>a)</code></pre></div>
 <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png" width="672"></p>
 <p>The endpoints obtained with this model are</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/endpoints.html">endpoints</a></span>(m.Z.mkin.5a)</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/endpoints.html">endpoints</a></span>(m.Z.mkin<span class="fl">.5</span>a)</code></pre></div>
 <pre><code>## $ff
 ##   Z0_free_Z1        Z1_Z2      Z2_sink   Z2_Z3_free Z3_free_sink 
 ##      1.00000      1.00000      0.46344      0.53656      1.00000 
 ## 
 ## $SFORB
 ##     Z0_b1     Z0_b2     Z3_b1     Z3_b2 
-## 2.4471373 0.0075126 0.0800076 0.0000000 
+## 2.4471382 0.0075127 0.0800075 0.0000000 
 ## 
 ## $distimes
 ##      DT50   DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2
-## Z0 0.3043 1.1848    0.28325     92.265         NA         NA
+## Z0 0.3043 1.1848    0.28325     92.264         NA         NA
 ## Z1 1.5148 5.0320         NA         NA         NA         NA
 ## Z2 1.6414 5.4526         NA         NA         NA         NA
 ## Z3     NA     NA         NA         NA     8.6635        Inf</code></pre>
@@ -291,7 +294,8 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
 
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
         <div id="tocnav">
-      <h2>Contents</h2>
+      <h2 class="hasAnchor">
+<a href="#tocnav" class="anchor"></a>Contents</h2>
       <ul class="nav nav-pills nav-stacked">
 <li><a href="#the-data">The data</a></li>
       <li><a href="#parent-and-one-metabolite">Parent and one metabolite</a></li>
@@ -310,7 +314,7 @@ FOCUS_2006_Z_mkin &lt;-<span class="st"> </span><span class="kw"><a href="../ref
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png
index cb32b3c6..b66289d9 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png
index 7ce8a39c..af26f416 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png
index a7528057..d4e7a647 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png
index 3fe80d42..8c6d81ef 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png
index ff0dbbbd..fffb4892 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png
index dfe447ed..0ac5b6ce 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
index 8f7102aa..87226454 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png
index 74a82e2b..58241d1d 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png
index 7042e390..381e7df5 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png
index 29824228..19e73f1c 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png
diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png
index 79f1b87e..891d8d92 100644
--- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png
+++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png
diff --git a/docs/articles/compiled_models.R b/docs/articles/compiled_models.R
deleted file mode 100644
index c5b06c11..00000000
--- a/docs/articles/compiled_models.R
+++ /dev/null
@@ -1,55 +0,0 @@
-## ---- include = FALSE----------------------------------------------------
-library(knitr)
-opts_chunk$set(tidy = FALSE, cache = FALSE)
-
-## ----check_gcc-----------------------------------------------------------
-Sys.which("gcc")
-
-## ----create_SFO_SFO------------------------------------------------------
-library("mkin", quietly = TRUE)
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
-
-## ----benchmark_SFO_SFO, fig.height = 3-----------------------------------
-if (require(rbenchmark)) {
-  b.1 <- benchmark(
-    "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                      solution_type = "deSolve",
-                                      use_compiled = FALSE, quiet = TRUE),
-    "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                 solution_type = "eigen", quiet = TRUE),
-    "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                  solution_type = "deSolve", quiet = TRUE),
-    replications = 3)
-  print(b.1)
-  factor_SFO_SFO <- round(b.1["1", "relative"])
-} else {
-  factor_SFO_SFO <- NA
-  print("R package benchmark is not available")
-}
-
-## ----benchmark_FOMC_SFO, fig.height = 3----------------------------------
-if (require(rbenchmark)) {
-  FOMC_SFO <- mkinmod(
-    parent = mkinsub("FOMC", "m1"),
-    m1 = mkinsub( "SFO"))
-
-  b.2 <- benchmark(
-    "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D,
-                                      use_compiled = FALSE, quiet = TRUE),
-    "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
-    replications = 3)
-  print(b.2)
-  factor_FOMC_SFO <- round(b.2["1", "relative"])
-} else {
-  factor_FOMC_SFO <- NA
-  print("R package benchmark is not available")
-}
-
-## ----sessionInfo, echo = FALSE-------------------------------------------
-cat(capture.output(sessionInfo())[1:3], sep = "\n")
-if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {
-  cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))
-}
-
diff --git a/docs/articles/compiled_models.html b/docs/articles/compiled_models.html
index d5d29a1a..9f0b5708 100644
--- a/docs/articles/compiled_models.html
+++ b/docs/articles/compiled_models.html
@@ -8,8 +8,11 @@
 <title>Performance benefit by using compiled model definitions in mkin • mkin</title>
 <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
 <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
-<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
+<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Performance benefit by using compiled model definitions in mkin">
+<meta property="og:description" content="">
+<meta name="twitter:card" content="summary">
+<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
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 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]-->
@@ -77,7 +80,7 @@
       <h1>Performance benefit by using compiled model definitions in mkin</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-16</h4>
+            <h4 class="date">2018-03-01</h4>
           </div>
 
     
@@ -97,73 +100,66 @@ SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mki
   <span class="dt">m1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
 <p>We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the benchmark package.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">if (<span class="kw">require</span>(rbenchmark)) {
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="cf">if</span> (<span class="kw">require</span>(rbenchmark)) {
   b<span class="fl">.1</span> &lt;-<span class="st"> </span><span class="kw">benchmark</span>(
-    <span class="st">"deSolve, not compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_2006_D,
+    <span class="st">"deSolve, not compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D,
                                       <span class="dt">solution_type =</span> <span class="st">"deSolve"</span>,
                                       <span class="dt">use_compiled =</span> <span class="ot">FALSE</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>),
-    <span class="st">"Eigenvalue based"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_2006_D,
+    <span class="st">"Eigenvalue based"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D,
                                  <span class="dt">solution_type =</span> <span class="st">"eigen"</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>),
-    <span class="st">"deSolve, compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_2006_D,
+    <span class="st">"deSolve, compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D,
                                   <span class="dt">solution_type =</span> <span class="st">"deSolve"</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>),
     <span class="dt">replications =</span> <span class="dv">3</span>)
   <span class="kw">print</span>(b<span class="fl">.1</span>)
   factor_SFO_SFO &lt;-<span class="st"> </span><span class="kw">round</span>(b<span class="fl">.1</span>[<span class="st">"1"</span>, <span class="st">"relative"</span>])
-} else {
+} <span class="cf">else</span> {
   factor_SFO_SFO &lt;-<span class="st"> </span><span class="ot">NA</span>
   <span class="kw">print</span>(<span class="st">"R package benchmark is not available"</span>)
 }</code></pre></div>
 <pre><code>## Lade nötiges Paket: rbenchmark</code></pre>
-<pre><code>##                    test replications elapsed relative user.self sys.self
-## 3     deSolve, compiled            3   1.940    1.000     1.940        0
-## 1 deSolve, not compiled            3  13.865    7.147    13.864        0
-## 2      Eigenvalue based            3   2.427    1.251     2.428        0
-##   user.child sys.child
-## 3          0         0
-## 1          0         0
-## 2          0         0</code></pre>
-<p>We see that using the compiled model is by a factor of around 7 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.</p>
+<pre><code>## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+## logical.return = TRUE, : es gibt kein Paket namens 'rbenchmark'</code></pre>
+<pre><code>## [1] "R package benchmark is not available"</code></pre>
+<p>We see that using the compiled model is by a factor of around NA faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.</p>
 </div>
 <div id="model-that-can-not-be-solved-with-eigenvalues" class="section level2">
 <h2 class="hasAnchor">
 <a href="#model-that-can-not-be-solved-with-eigenvalues" class="anchor"></a>Model that can not be solved with Eigenvalues</h2>
 <p>This evaluation is also taken from the example section of mkinfit.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">if (<span class="kw">require</span>(rbenchmark)) {
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="cf">if</span> (<span class="kw">require</span>(rbenchmark)) {
   FOMC_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(
     <span class="dt">parent =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"FOMC"</span>, <span class="st">"m1"</span>),
     <span class="dt">m1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>( <span class="st">"SFO"</span>))
 
   b<span class="fl">.2</span> &lt;-<span class="st"> </span><span class="kw">benchmark</span>(
-    <span class="st">"deSolve, not compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(FOMC_SFO, FOCUS_2006_D,
+    <span class="st">"deSolve, not compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(FOMC_SFO, FOCUS_<span class="dv">2006</span>_D,
                                       <span class="dt">use_compiled =</span> <span class="ot">FALSE</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>),
-    <span class="st">"deSolve, compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(FOMC_SFO, FOCUS_2006_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>),
+    <span class="st">"deSolve, compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(FOMC_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>),
     <span class="dt">replications =</span> <span class="dv">3</span>)
   <span class="kw">print</span>(b<span class="fl">.2</span>)
   factor_FOMC_SFO &lt;-<span class="st"> </span><span class="kw">round</span>(b<span class="fl">.2</span>[<span class="st">"1"</span>, <span class="st">"relative"</span>])
-} else {
+} <span class="cf">else</span> {
   factor_FOMC_SFO &lt;-<span class="st"> </span><span class="ot">NA</span>
   <span class="kw">print</span>(<span class="st">"R package benchmark is not available"</span>)
 }</code></pre></div>
-<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<pre><code>##                    test replications elapsed relative user.self sys.self
-## 2     deSolve, compiled            3   3.432    1.000     3.428        0
-## 1 deSolve, not compiled            3  28.844    8.404    28.840        0
-##   user.child sys.child
-## 2          0         0
-## 1          0         0</code></pre>
-<p>Here we get a performance benefit of a factor of 8 using the version of the differential equation model compiled from C code!</p>
-<p>This vignette was built with mkin 0.9.47.1 on</p>
+<pre><code>## Lade nötiges Paket: rbenchmark</code></pre>
+<pre><code>## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+## logical.return = TRUE, : es gibt kein Paket namens 'rbenchmark'</code></pre>
+<pre><code>## [1] "R package benchmark is not available"</code></pre>
+<p>Here we get a performance benefit of a factor of NA using the version of the differential equation model compiled from C code!</p>
+<p>This vignette was built with mkin 0.9.46.3 on</p>
 <pre><code>## R version 3.4.3 (2017-11-30)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
 ## Running under: Debian GNU/Linux 9 (stretch)</code></pre>
-<pre><code>## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz</code></pre>
+<pre><code>## CPU model: AMD Ryzen 7 1700 Eight-Core Processor</code></pre>
 </div>
 </div>
   </div>
 
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
         <div id="tocnav">
-      <h2>Contents</h2>
+      <h2 class="hasAnchor">
+<a href="#tocnav" class="anchor"></a>Contents</h2>
       <ul class="nav nav-pills nav-stacked">
 <li><a href="#model-that-can-also-be-solved-with-eigenvalues">Model that can also be solved with Eigenvalues</a></li>
       <li><a href="#model-that-can-not-be-solved-with-eigenvalues">Model that can not be solved with Eigenvalues</a></li>
@@ -179,7 +175,7 @@ SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mki
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/articles/compiled_models_files/figure-html/benchmark_FOMC_SFO-1.png b/docs/articles/compiled_models_files/figure-html/benchmark_FOMC_SFO-1.png
deleted file mode 100644
index dae2c9d4..00000000
--- a/docs/articles/compiled_models_files/figure-html/benchmark_FOMC_SFO-1.png
+++ /dev/null
diff --git a/docs/articles/compiled_models_files/figure-html/benchmark_SFO_SFO-1.png b/docs/articles/compiled_models_files/figure-html/benchmark_SFO_SFO-1.png
deleted file mode 100644
index 696236dc..00000000
--- a/docs/articles/compiled_models_files/figure-html/benchmark_SFO_SFO-1.png
+++ /dev/null
diff --git a/docs/articles/header.tex b/docs/articles/header.tex
deleted file mode 100644
index b8644ae2..00000000
--- a/docs/articles/header.tex
+++ /dev/null
@@ -1,22 +0,0 @@
-\usepackage{booktabs}
-\usepackage{amsfonts}
-\usepackage{latexsym}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{graphicx}
-\usepackage{parskip}
-\usepackage[round]{natbib}
-\usepackage{amstext}
-\usepackage{hyperref}
-
-\newcommand{\Rpackage}[1]{{\normalfont\fontseries{b}\selectfont #1}}
-\newcommand{\Robject}[1]{\texttt{#1}}
-\newcommand{\Rclass}[1]{\textit{#1}}
-\newcommand{\Rcmd}[1]{\texttt{#1}}
-
-\newcommand{\RR}{\textsf{R}}
-
-\RequirePackage[T1]{fontenc}
-\RequirePackage{graphicx,ae,fancyvrb}
-\IfFileExists{upquote.sty}{\RequirePackage{upquote}}{}
-\usepackage{relsize}
diff --git a/docs/articles/index.html b/docs/articles/index.html
index c1dc0b64..2b16580a 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -18,12 +18,16 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
-
+  
+  
+<meta property="og:title" content="Articles" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -123,7 +127,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/articles/mkin.R b/docs/articles/mkin.R
deleted file mode 100644
index 19e80322..00000000
--- a/docs/articles/mkin.R
+++ /dev/null
@@ -1,34 +0,0 @@
-## ---- include = FALSE----------------------------------------------------
-require(knitr)
-opts_chunk$set(engine='R', tidy=FALSE)
-
-## ---- echo = TRUE, cache = TRUE, fig = TRUE, fig.width = 8, fig.height = 7----
-library("mkin", quietly = TRUE)
-# Define the kinetic model
-m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
-                         M1 = mkinsub("SFO", "M2"),
-                         M2 = mkinsub("SFO"),
-                         use_of_ff = "max", quiet = TRUE)
-
-
-# Produce model predictions using some arbitrary parameters
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-d_SFO_SFO_SFO <- mkinpredict(m_SFO_SFO_SFO,
-  c(k_parent = 0.03,
-    f_parent_to_M1 = 0.5, k_M1 = log(2)/100,
-    f_M1_to_M2 = 0.9, k_M2 = log(2)/50),
-  c(parent = 100, M1 = 0, M2 = 0),
-  sampling_times)
-
-# Generate a dataset by adding normally distributed errors with
-# standard deviation 3, for two replicates at each sampling time
-d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, reps = 2,
-                             sdfunc = function(x) 3,
-                             n = 1, seed = 123456789 )
-
-# Fit the model to the dataset
-f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)
-
-# Plot the results separately for parent and metabolites
-plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))
-
diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html
index b70918ab..a91da0a4 100644
--- a/docs/articles/mkin.html
+++ b/docs/articles/mkin.html
@@ -8,8 +8,11 @@
 <title>Introduction to mkin • mkin</title>
 <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
 <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
-<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
+<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Introduction to mkin">
+<meta property="og:description" content="">
+<meta name="twitter:card" content="summary">
+<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]-->
@@ -77,7 +80,7 @@
       <h1>Introduction to mkin</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-16</h4>
+            <h4 class="date">2018-03-01</h4>
           </div>
 
     
@@ -100,15 +103,15 @@ m_SFO_SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../referen
 sampling_times =<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)
 d_SFO_SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinpredict.html">mkinpredict</a></span>(m_SFO_SFO_SFO,
   <span class="kw">c</span>(<span class="dt">k_parent =</span> <span class="fl">0.03</span>,
-    <span class="dt">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="dt">k_M1 =</span> <span class="kw">log</span>(<span class="dv">2</span>)/<span class="dv">100</span>,
-    <span class="dt">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="dt">k_M2 =</span> <span class="kw">log</span>(<span class="dv">2</span>)/<span class="dv">50</span>),
+    <span class="dt">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="dt">k_M1 =</span> <span class="kw">log</span>(<span class="dv">2</span>)<span class="op">/</span><span class="dv">100</span>,
+    <span class="dt">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="dt">k_M2 =</span> <span class="kw">log</span>(<span class="dv">2</span>)<span class="op">/</span><span class="dv">50</span>),
   <span class="kw">c</span>(<span class="dt">parent =</span> <span class="dv">100</span>, <span class="dt">M1 =</span> <span class="dv">0</span>, <span class="dt">M2 =</span> <span class="dv">0</span>),
   sampling_times)
 
 <span class="co"># Generate a dataset by adding normally distributed errors with</span>
 <span class="co"># standard deviation 3, for two replicates at each sampling time</span>
 d_SFO_SFO_SFO_err &lt;-<span class="st"> </span><span class="kw"><a href="../reference/add_err.html">add_err</a></span>(d_SFO_SFO_SFO, <span class="dt">reps =</span> <span class="dv">2</span>,
-                             <span class="dt">sdfunc =</span> function(x) <span class="dv">3</span>,
+                             <span class="dt">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,
                              <span class="dt">n =</span> <span class="dv">1</span>, <span class="dt">seed =</span> <span class="dv">123456789</span> )
 
 <span class="co"># Fit the model to the dataset</span>
@@ -208,7 +211,8 @@ f_SFO_SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../referen
 
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
         <div id="tocnav">
-      <h2>Contents</h2>
+      <h2 class="hasAnchor">
+<a href="#tocnav" class="anchor"></a>Contents</h2>
       <ul class="nav nav-pills nav-stacked">
 <li><a href="#abstract">Abstract</a></li>
       <li>
@@ -236,7 +240,7 @@ f_SFO_SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../referen
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
index fafe8afd..5a3e3b6c 100644
--- a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
+++ b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
diff --git a/docs/articles/twa.R b/docs/articles/twa.R
deleted file mode 100644
index 8b137891..00000000
--- a/docs/articles/twa.R
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/docs/articles/twa.html b/docs/articles/twa.html
index 086c8593..400b1383 100644
--- a/docs/articles/twa.html
+++ b/docs/articles/twa.html
@@ -8,8 +8,11 @@
 <title>Calculation of time weighted average concentrations with mkin • mkin</title>
 <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
 <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
-<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
-<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
+<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Calculation of time weighted average concentrations with mkin">
+<meta property="og:description" content="">
+<meta name="twitter:card" content="summary">
+<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]-->
@@ -77,7 +80,7 @@
       <h1>Calculation of time weighted average concentrations with mkin</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2018-01-16</h4>
+            <h4 class="date">2018-03-01</h4>
           </div>
 
     
@@ -126,7 +129,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/authors.html b/docs/authors.html
index 7f3918c8..7abb53de 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -18,12 +18,16 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="pkgdown.css" rel="stylesheet">
 <script src="jquery.sticky-kit.min.js"></script>
 <script src="pkgdown.js"></script>
-
+  
+  
+<meta property="og:title" content="Authors" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -103,19 +107,19 @@
 
     <ul class="list-unstyled">
       <li>
-        <p><strong>Johannes Ranke</strong>. Author, maintainer, copyright&nbsp;holder.
-        <br /><small>0000-0003-4371-6538</small></p>
+        <p><strong>Johannes Ranke</strong>. Author, maintainer, copyright holder. <a href='https://orcid.org/0000-0003-4371-6538' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid'></a>
+        </p>
       </li>
       <li>
-        <p><strong>Katrin Lindenberger</strong>. Contributor.
+        <p><strong>Katrin Lindenberger</strong>. Contributor. 
         </p>
       </li>
       <li>
-        <p><strong>René Lehmann</strong>. Contributor.
+        <p><strong>René Lehmann</strong>. Contributor. 
         </p>
       </li>
       <li>
-        <p><strong>Eurofins Regulatory AG</strong>. Copyright&nbsp;holder.
+        <p><strong>Eurofins Regulatory AG</strong>. Copyright holder. 
         </p>
       </li>
     </ul>
@@ -131,7 +135,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/index.html b/docs/index.html
index 2f46d730..c20d124b 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -8,8 +8,18 @@
 <title>Kinetic Evaluation of Chemical Degradation Data • mkin</title>
 <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous">
 <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
-<!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
-<script src="jquery.sticky-kit.min.js"></script><script src="pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
+<script src="jquery.sticky-kit.min.js"></script><script src="pkgdown.js"></script><meta property="og:title" content="Kinetic Evaluation of Chemical Degradation Data">
+<meta property="og:description" content="Calculation routines based on the FOCUS Kinetics Report (2006,
+  2014).  Includes a function for conveniently defining differential equation
+  models, model solution based on eigenvalues if possible or using numerical
+  solvers and a choice of the optimisation methods made available by the 'FME'
+  package.  If a C compiler (on windows: 'Rtools') is installed, differential
+  equation models are solved using compiled C functions.  Please note that no
+  warranty is implied for correctness of results or fitness for a particular
+  purpose.">
+<meta name="twitter:card" content="summary">
+<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]-->
@@ -159,8 +169,7 @@
 <p>GPL</p>
 <h2>Developers</h2>
 <ul class="list-unstyled">
-<li>Johannes Ranke <br><small class="roles"> Author, maintainer, copyright holder </small> <br><small>(0000-0003-4371-6538)</small>
-</li>
+<li>Johannes Ranke <br><small class="roles"> Author, maintainer, copyright holder </small> <a href="https://orcid.org/0000-0003-4371-6538" target="orcid.widget"><img src="https://members.orcid.org/sites/default/files/vector_iD_icon.svg" class="orcid"></a> </li>
 <li><a href="authors.html">All authors...</a></li>
 </ul>
 <h2>Dev status</h2>
@@ -176,7 +185,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/news/index.html b/docs/news/index.html
index 64e4e9bc..dfb70875 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -18,12 +18,16 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
-
+  
+  
+<meta property="og:title" content="All news" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -629,7 +633,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/pkgdown.css b/docs/pkgdown.css
index fd7b0ba4..181fe639 100644
--- a/docs/pkgdown.css
+++ b/docs/pkgdown.css
@@ -34,13 +34,14 @@ img.icon {
   float: right;
 }
 
-/* Section anchors ---------------------------------*/
-
-.hasAnchor {
-  margin-left: -30px;
+img {
+  max-width: 100%;
 }
 
+/* Section anchors ---------------------------------*/
+
 a.anchor {
+  margin-left: -30px;
   display:inline-block;
   width: 30px;
   height: 30px;
@@ -56,6 +57,13 @@ a.anchor {
   visibility: visible;
 }
 
+@media (max-width: 767px) {
+  .hasAnchor:hover a.anchor {
+    visibility: hidden;
+  }
+}
+
+
 /* Fixes for fixed navbar --------------------------*/
 
 .contents h1, .contents h2, .contents h3, .contents h4 {
@@ -63,6 +71,17 @@ a.anchor {
   margin-top: -60px;
 }
 
+/* Static header placement on mobile devices */
+@media (max-width: 767px) {
+  .navbar-fixed-top {
+    position: absolute;
+  }
+  .navbar {
+    padding: 0;
+  }
+}
+
+
 /* Sidebar --------------------------*/
 
 #sidebar {
@@ -81,33 +100,95 @@ a.anchor {
   margin-bottom: 0.5em;
 }
 
+.orcid {
+  height: 16px;
+  vertical-align: middle;
+}
+
+/* Reference index & topics ----------------------------------------------- */
+
+.ref-index th {font-weight: normal;}
+.ref-index h2 {font-size: 20px;}
+
+.ref-index td {vertical-align: top;}
+.ref-index .alias {width: 40%;}
+.ref-index .title {width: 60%;}
+
+.ref-index .alias {width: 40%;}
+.ref-index .title {width: 60%;}
+
+.ref-arguments th {text-align: right; padding-right: 10px;}
+.ref-arguments th, .ref-arguments td {vertical-align: top;}
+.ref-arguments .name {width: 20%;}
+.ref-arguments .desc {width: 80%;}
+
+/* Nice scrolling for wide elements --------------------------------------- */
+
+table {
+  display: block;
+  overflow: auto;
+}
+
 /* Syntax highlighting ---------------------------------------------------- */
 
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-  background-color: #f7f7f7;
-  color: #333;
+pre {
+  word-wrap: normal;
+  word-break: normal;
+  border: 1px solid #eee;
 }
-code a {
-  color: #375f84;
+
+pre, code {
+  background-color: #f8f8f8;
+  color: #333;
 }
 
-.warning { color: red; }
-.message { font-weight: bolder; }
-.error   { color: red; font-weight: bolder; }
+pre code {
+  overflow: auto;
+  word-wrap: normal;
+  white-space: pre;
+}
 
-.fl,.number {color:rgb(21,20,181);}
-.fu,.functioncall {color:#264D66 ;}
-.ch,.st,.string {color:#375D81 ;}
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-.eqformalargs {color:#264D66;}
-.slot {font-style:italic;}
-.symbol {color:black ;}
-.prompt {color:black ;}
+pre .img {
+  margin: 5px 0;
+}
 
-pre img {
+pre .img img {
   background-color: #fff;
   display: block;
+  height: auto;
+}
+
+code a, pre a {
+  color: #375f84;
+}
+
+a.sourceLine:hover {
+  text-decoration: none;
+}
+
+.fl      {color: #1514b5;}
+.fu      {color: #000000;} /* function */
+.ch,.st  {color: #036a07;} /* string */
+.kw      {color: #264D66;} /* keyword */
+.co      {color: #888888;} /* comment */
+
+.message { color: black;   font-weight: bolder;}
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+.warning { color: #6A0366; font-weight: bolder;}
+
+/* Clipboard --------------------------*/
+
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+  position: relative;
+}
+
+.btn-copy-ex {
+  position: absolute;
+  right: 0;
+  top: 0;
+  visibility: hidden;
+}
+
+.hasCopyButton:hover button.btn-copy-ex {
+  visibility: visible;
 }
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index c8b38c49..64b20df4 100644
--- a/docs/pkgdown.js
+++ b/docs/pkgdown.js
@@ -1,8 +1,94 @@
 $(function() {
-  $("#sidebar").stick_in_parent({offset_top: 40});
+
+  $("#sidebar")
+    .stick_in_parent({offset_top: 40})
+    .on('sticky_kit:bottom', function(e) {
+      $(this).parent().css('position', 'static');
+    })
+    .on('sticky_kit:unbottom', function(e) {
+      $(this).parent().css('position', 'relative');
+    });
+
   $('body').scrollspy({
     target: '#sidebar',
     offset: 60
   });
 
+  var cur_path = paths(location.pathname);
+  $("#navbar ul li a").each(function(index, value) {
+    if (value.text == "Home")
+      return;
+    if (value.getAttribute("href") === "#")
+      return;
+
+    var path = paths(value.pathname);
+    if (is_prefix(cur_path, path)) {
+      // Add class to parent <li>, and enclosing <li> if in dropdown
+      var menu_anchor = $(value);
+      menu_anchor.parent().addClass("active");
+      menu_anchor.closest("li.dropdown").addClass("active");
+    }
+  });
 });
+
+function paths(pathname) {
+  var pieces = pathname.split("/");
+  pieces.shift(); // always starts with /
+
+  var end = pieces[pieces.length - 1];
+  if (end === "index.html" || end === "")
+    pieces.pop();
+  return(pieces);
+}
+
+function is_prefix(needle, haystack) {
+  if (needle.length > haystack.lengh)
+    return(false);
+
+  for (var i = 0; i < haystack.length; i++) {
+    if (needle[i] != haystack[i])
+      return(false);
+  }
+
+  return(true);
+}
+
+/* Clipboard --------------------------*/
+
+function changeTooltipMessage(element, msg) {
+  var tooltipOriginalTitle=element.getAttribute('data-original-title');
+  element.setAttribute('data-original-title', msg);
+  $(element).tooltip('show');
+  element.setAttribute('data-original-title', tooltipOriginalTitle);
+}
+
+if(Clipboard.isSupported()) {
+  $(document).ready(function() {
+    var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-hidden='true' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy' aria-hidden='true'></i></button>";
+
+    $(".examples").addClass("hasCopyButton");
+
+    // Insert copy buttons:
+    $(copyButton).prependTo(".hasCopyButton");
+
+    // Initialize tooltips:
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+
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+
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+      e.clearSelection();
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+      changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
+    });
+  });
+}
+
diff --git a/docs/reference/DFOP.solution-1.png b/docs/reference/DFOP.solution-1.png
new file mode 100644
index 00000000..1549a73b
--- /dev/null
+++ b/docs/reference/DFOP.solution-1.png
diff --git a/docs/reference/DFOP.solution-2.png b/docs/reference/DFOP.solution-2.png
deleted file mode 100644
index 0902b9df..00000000
--- a/docs/reference/DFOP.solution-2.png
+++ /dev/null
diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html
index 30b9d057..ff1ad823 100644
--- a/docs/reference/DFOP.solution.html
+++ b/docs/reference/DFOP.solution.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Double First-Order in Parallel kinetics — DFOP.solution" />
 
+<meta property="og:description" content="Function describing decline from a defined starting value using the sum
+  of two exponential decline functions." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +78,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +94,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -146,11 +152,11 @@
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>DFOP.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.3</span>), <span class='fl'>0</span>, <span class='fl'>4</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'>c</span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><img src='DFOP.solution-2.png' alt='' width='540' height='400' /></pre>
+    <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>DFOP.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.3</span>), <span class='fl'>0</span>, <span class='fl'>4</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'>c</span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><div class='img'><img src='DFOP.solution-1.png' alt='' width='700' height='432.632880098887' /></div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -173,7 +179,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html
index 5d4eca29..11738484 100644
--- a/docs/reference/Extract.mmkin.html
+++ b/docs/reference/Extract.mmkin.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Subsetting method for mmkin objects — [.mmkin" />
 
+<meta property="og:description" content="Subsetting method for mmkin objects." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +77,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -147,16 +152,16 @@
                 <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
   <span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ]</div><div class='output co'>#&gt;       dataset
 #&gt; model  B       C      
-#&gt;   FOMC List,42 List,42
+#&gt;   FOMC List,45 List,45
 #&gt; attr(,"class")
 #&gt; [1] "mmkin"</div><div class='input'>  <span class='no'>fits</span>[, <span class='st'>"B"</span>]</div><div class='output co'>#&gt;       dataset
 #&gt; model  B      
-#&gt;   SFO  List,42
-#&gt;   FOMC List,42
+#&gt;   SFO  List,45
+#&gt;   FOMC List,45
 #&gt; attr(,"class")
 #&gt; [1] "mmkin"</div><div class='input'>  <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>]</div><div class='output co'>#&gt;      dataset
 #&gt; model B      
-#&gt;   SFO List,42
+#&gt;   SFO List,45
 #&gt; attr(,"class")
 #&gt; [1] "mmkin"</div><div class='input'>
   <span class='fu'>head</span>(
@@ -164,7 +169,7 @@
     <span class='no'>fits</span><span class='kw'>[[</span><span class='st'>"FOMC"</span>, <span class='st'>"B"</span>]]
   )</div><div class='output co'>#&gt; $par
 #&gt;  parent_0 log_alpha  log_beta 
-#&gt; 99.666192  2.549849  5.050586 
+#&gt; 99.666193  2.549849  5.050586 
 #&gt; 
 #&gt; $ssr
 #&gt; [1] 28.58291
@@ -180,7 +185,7 @@
 #&gt;       25       78 
 #&gt; 
 #&gt; $counts
-#&gt; [1] "both X-convergence and relative convergence (5)"
+#&gt; [1] "relative convergence (4)"
 #&gt; </div><div class='input'>
   <span class='fu'>head</span>(
   <span class='co'># The same can be achieved by</span>
@@ -258,7 +263,7 @@
 #&gt; 
 #&gt; $time
 #&gt;    user  system elapsed 
-#&gt;   0.064   0.000   0.063 
+#&gt;   0.058   0.000   0.057 
 #&gt; 
 #&gt; $mkinmod
 #&gt; &lt;mkinmod&gt; model generated with
@@ -398,7 +403,7 @@
 #&gt; {
 #&gt;     assign("calls", calls + 1, inherits = TRUE)
 #&gt;     if (trace_parms) 
-#&gt;         cat(P, "\\n")
+#&gt;         cat(P, "\n")
 #&gt;     if (length(state.ini.optim) &gt; 0) {
 #&gt;         odeini &lt;- c(P[1:length(state.ini.optim)], state.ini.fixed)
 #&gt;         names(odeini) &lt;- c(state.ini.optim.boxnames, state.ini.fixed.boxnames)
@@ -420,7 +425,7 @@
 #&gt;     if (mC$model &lt; cost.old) {
 #&gt;         if (!quiet) 
 #&gt;             cat("Model cost at call ", calls, ": ", mC$model, 
-#&gt;                 "\\n")
+#&gt;                 "\n")
 #&gt;         if (plot) {
 #&gt;             outtimes_plot = seq(min(observed$time), max(observed$time), 
 #&gt;                 length.out = 100)
@@ -447,8 +452,8 @@
 #&gt;     }
 #&gt;     return(mC)
 #&gt; }
-#&gt; &lt;bytecode: 0x560110508c60&gt;
-#&gt; &lt;environment: 0x56010f8d8c30&gt;
+#&gt; &lt;bytecode: 0x55555ad80908&gt;
+#&gt; &lt;environment: 0x55555b1b4b90&gt;
 #&gt; 
 #&gt; $cost_notrans
 #&gt; function (P) 
@@ -470,8 +475,8 @@
 #&gt;         scaleVar = scaleVar)
 #&gt;     return(mC)
 #&gt; }
-#&gt; &lt;bytecode: 0x56010fede550&gt;
-#&gt; &lt;environment: 0x56010f8d8c30&gt;
+#&gt; &lt;bytecode: 0x55555b174428&gt;
+#&gt; &lt;environment: 0x55555b1b4b90&gt;
 #&gt; 
 #&gt; $hessian_notrans
 #&gt;                   parent_0 k_parent_sink
@@ -512,6 +517,10 @@
 #&gt; $weight.ini
 #&gt; [1] "none"
 #&gt; 
+#&gt; $tc.ini
+#&gt; sigma_low  rsd_high 
+#&gt;      0.50      0.07 
+#&gt; 
 #&gt; $reweight.tol
 #&gt; [1] 1e-08
 #&gt; 
@@ -534,7 +543,13 @@
 #&gt; 99.17407 
 #&gt; 
 #&gt; $date
-#&gt; [1] "Sat Jul 29 15:14:04 2017"
+#&gt; [1] "Thu Mar  1 14:26:09 2018"
+#&gt; 
+#&gt; $version
+#&gt; [1] "0.9.47.1"
+#&gt; 
+#&gt; $Rversion
+#&gt; [1] "3.4.3"
 #&gt; 
 #&gt; attr(,"class")
 #&gt; [1] "mkinfit" "modFit" 
@@ -563,7 +578,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html
index d79aaff6..5fb3ccfe 100644
--- a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html
+++ b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html
@@ -18,12 +18,23 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B" />
 
+<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+generated with different software packages. Taken directly from FOCUS (2006).
+The results from fitting the data with the Topfit software was removed, as
+the initial concentration of the parent compound was fixed to a value of 100
+in this fit." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +81,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +97,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -132,7 +141,7 @@ in this fit.</p>
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
@@ -158,7 +167,7 @@ in this fit.</p>
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html
index e0da5a8d..c1665dee 100644
--- a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html
+++ b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html
@@ -18,12 +18,23 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F" />
 
+<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+generated with different software packages. Taken directly from FOCUS (2006).
+The results from fitting the data with the Topfit software was removed, as
+the initial concentration of the parent compound was fixed to a value of 100
+in this fit." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +81,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +97,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -131,7 +140,7 @@ in this fit.</p>
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
@@ -157,7 +166,7 @@ in this fit.</p>
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html
index 720073f2..db3d228d 100644
--- a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html
+++ b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html
@@ -18,12 +18,23 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F" />
 
+<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+generated with different software packages. Taken directly from FOCUS (2006).
+The results from fitting the data with the Topfit software was removed, as
+the initial concentration of the parent compound was fixed to a value of 100
+in this fit." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +81,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +97,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -132,7 +141,7 @@ in this fit.</p>
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
@@ -158,7 +167,7 @@ in this fit.</p>
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html
index b12abf8c..cc4fcb1d 100644
--- a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html
+++ b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html
@@ -18,12 +18,23 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F" />
 
+<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values
+generated with different software packages. Taken directly from FOCUS (2006).
+The results from fitting the data with the Topfit software was removed, as
+the initial concentration of the parent compound was fixed to a value of 100
+in this fit." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +81,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +97,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -130,7 +139,7 @@ in this fit.</p>
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
@@ -156,7 +165,7 @@ in this fit.</p>
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/FOCUS_2006_datasets.html b/docs/reference/FOCUS_2006_datasets.html
index 65bc572a..4548c983 100644
--- a/docs/reference/FOCUS_2006_datasets.html
+++ b/docs/reference/FOCUS_2006_datasets.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets" />
 
+<meta property="og:description" content="Data taken from FOCUS (2006), p. 258." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +77,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -123,7 +128,7 @@
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
@@ -158,7 +163,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/FOMC.solution-1.png b/docs/reference/FOMC.solution-1.png
new file mode 100644
index 00000000..58178df5
--- /dev/null
+++ b/docs/reference/FOMC.solution-1.png
diff --git a/docs/reference/FOMC.solution-2.png b/docs/reference/FOMC.solution-2.png
deleted file mode 100644
index a673bc0e..00000000
--- a/docs/reference/FOMC.solution-2.png
+++ /dev/null
diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html
index 9af30b7a..810b0eba 100644
--- a/docs/reference/FOMC.solution.html
+++ b/docs/reference/FOMC.solution.html
@@ -18,12 +18,23 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
-
+  
+  
+<meta property="og:title" content="First-Order Multi-Compartment kinetics — FOMC.solution" />
+
+<meta property="og:description" content="Function describing exponential decline from a defined starting value, with 
+  a decreasing rate constant.
+The form given here differs slightly from the original reference by Gustafson
+  and Holden (1990). The parameter beta corresponds to 1/beta in the
+  original equation." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +81,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +97,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -152,14 +161,14 @@
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
 <p>Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil: A
   new model based on spatial variability. <em>Environmental Science and 
   Technology</em> <b>24</b>, 1032-1038</p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>FOMC.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>10</span>, <span class='fl'>2</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><img src='FOMC.solution-2.png' alt='' width='540' height='400' /></pre>
+    <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>FOMC.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>10</span>, <span class='fl'>2</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><div class='img'><img src='FOMC.solution-1.png' alt='' width='700' height='432.632880098887' /></div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -184,7 +193,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/HS.solution-1.png b/docs/reference/HS.solution-1.png
new file mode 100644
index 00000000..e259134e
--- /dev/null
+++ b/docs/reference/HS.solution-1.png
diff --git a/docs/reference/HS.solution-2.png b/docs/reference/HS.solution-2.png
deleted file mode 100644
index 2e516447..00000000
--- a/docs/reference/HS.solution-2.png
+++ /dev/null
diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html
index 8f6bbbe2..79358efb 100644
--- a/docs/reference/HS.solution.html
+++ b/docs/reference/HS.solution.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Hockey-Stick kinetics — HS.solution" />
 
+<meta property="og:description" content="Function describing two exponential decline functions with a break point
+  between them." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +78,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +94,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -147,11 +153,11 @@
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>HS.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>2</span>, <span class='fl'>0.3</span>, <span class='fl'>0.5</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'>c</span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><img src='HS.solution-2.png' alt='' width='540' height='400' /></pre>
+    <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>HS.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>2</span>, <span class='fl'>0.3</span>, <span class='fl'>0.5</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'>c</span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><div class='img'><img src='HS.solution-1.png' alt='' width='700' height='432.632880098887' /></div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -174,7 +180,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/IORE.solution-1.png b/docs/reference/IORE.solution-1.png
new file mode 100644
index 00000000..674c25d3
--- /dev/null
+++ b/docs/reference/IORE.solution-1.png
diff --git a/docs/reference/IORE.solution-2.png b/docs/reference/IORE.solution-2.png
deleted file mode 100644
index a83d49c7..00000000
--- a/docs/reference/IORE.solution-2.png
+++ /dev/null
diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html
index 45e090c0..f705ab2f 100644
--- a/docs/reference/IORE.solution.html
+++ b/docs/reference/IORE.solution.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Indeterminate order rate equation kinetics — IORE.solution" />
 
+<meta property="og:description" content="Function describing exponential decline from a defined starting value, with 
+  a concentration dependent rate constant." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +94,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -154,7 +157,7 @@
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>IORE.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.2</span>, <span class='fl'>1.3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>,
-                                       <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><img src='IORE.solution-2.png' alt='' width='540' height='400' /><div class='input'>  <span class='no'>fit.fomc</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+                                       <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><div class='img'><img src='IORE.solution-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>  <span class='no'>fit.fomc</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
   <span class='no'>fit.iore</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"IORE"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
   <span class='no'>fit.iore.deS</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"IORE"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
 
@@ -191,7 +194,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/SFO.solution-2.png b/docs/reference/SFO.solution-2.png
deleted file mode 100644
index 9626091f..00000000
--- a/docs/reference/SFO.solution-2.png
+++ /dev/null
diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html
index ef9b8eb7..a7934a35 100644
--- a/docs/reference/SFO.solution.html
+++ b/docs/reference/SFO.solution.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Single First-Order kinetics — SFO.solution" />
 
+<meta property="og:description" content="Function describing exponential decline from a defined starting value." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +77,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -136,11 +141,13 @@
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFO.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)</div><img src='SFO.solution-2.png' alt='' width='540' height='400' /></pre>
+    <pre class="examples"><div class='input'>  </div><span class='co'># NOT RUN {</span>
+<span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFO.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)
+<span class='co'># }</span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -163,7 +170,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/SFORB.solution-2.png b/docs/reference/SFORB.solution-2.png
deleted file mode 100644
index 63a50bf9..00000000
--- a/docs/reference/SFORB.solution-2.png
+++ /dev/null
diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html
index ebe67733..aaae7cdd 100644
--- a/docs/reference/SFORB.solution.html
+++ b/docs/reference/SFORB.solution.html
@@ -18,12 +18,23 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Single First-Order Reversible Binding kinetics — SFORB.solution" />
 
+<meta property="og:description" content="Function describing the solution of the differential equations describing
+  the kinetic model with first-order terms for a two-way transfer from a free
+  to a bound fraction, and a first-order degradation term for the free
+  fraction.  The initial condition is a defined amount in the free fraction and
+  no substance in the bound fraction." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +81,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +97,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -149,11 +158,13 @@
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>  <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFORB.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)</div><img src='SFORB.solution-2.png' alt='' width='540' height='400' /></pre>
+    <pre class="examples"><div class='input'>  </div><span class='co'># NOT RUN {</span>
+<span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFORB.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)
+<span class='co'># }</span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -176,7 +187,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/add_err-1.png b/docs/reference/add_err-1.png
new file mode 100644
index 00000000..4f9b1534
--- /dev/null
+++ b/docs/reference/add_err-1.png
diff --git a/docs/reference/add_err-2.png b/docs/reference/add_err-2.png
new file mode 100644
index 00000000..8fcf4625
--- /dev/null
+++ b/docs/reference/add_err-2.png
diff --git a/docs/reference/add_err-3.png b/docs/reference/add_err-3.png
new file mode 100644
index 00000000..e44839a6
--- /dev/null
+++ b/docs/reference/add_err-3.png
diff --git a/docs/reference/add_err-4.png b/docs/reference/add_err-4.png
deleted file mode 100644
index 8bbd1758..00000000
--- a/docs/reference/add_err-4.png
+++ /dev/null
diff --git a/docs/reference/add_err-6.png b/docs/reference/add_err-6.png
deleted file mode 100644
index 2a4fe33f..00000000
--- a/docs/reference/add_err-6.png
+++ /dev/null
diff --git a/docs/reference/add_err-8.png b/docs/reference/add_err-8.png
deleted file mode 100644
index 49c4a5f0..00000000
--- a/docs/reference/add_err-8.png
+++ /dev/null
diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html
index d56a8728..42bec993 100644
--- a/docs/reference/add_err.html
+++ b/docs/reference/add_err.html
@@ -18,12 +18,21 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
-
+  
+  
+<meta property="og:title" content="Add normally distributed errors to simulated kinetic degradation data — add_err" />
+
+<meta property="og:description" content="Normally distributed errors are added to data predicted for a specific
+  degradation model using mkinpredict. The variance of the error
+  may depend on the predicted value and is specified as a standard deviation." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -195,14 +204,14 @@
                    <span class='no'>d_SFO_SFO_err</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>,
                    <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)
 
-<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span>)</div><img src='add_err-4.png' alt='' width='540' height='400' /><div class='input'>
+<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span>)</div><div class='img'><img src='add_err-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>
 <span class='co'># We would like to inspect the fit for dataset 3 more closely</span>
 <span class='co'># Using double brackets makes the returned object an mkinfit object</span>
 <span class='co'># instead of a list of mkinfit objects, so plot.mkinfit is used</span>
-<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span><span class='kw'>[[</span><span class='fl'>3</span>]], <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><img src='add_err-6.png' alt='' width='540' height='400' /><div class='input'>
+<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span><span class='kw'>[[</span><span class='fl'>3</span>]], <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='add_err-2.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>
 <span class='co'># If we use single brackets, we should give two indices (model and dataset),</span>
 <span class='co'># and plot.mmkin is used</span>
-<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span>[<span class='fl'>1</span>, <span class='fl'>3</span>])</div><img src='add_err-8.png' alt='' width='540' height='400' /><div class='input'>
+<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span>[<span class='fl'>1</span>, <span class='fl'>3</span>])</div><div class='img'><img src='add_err-3.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>
 </div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
@@ -230,7 +239,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html
index ed235a47..c45a0b7c 100644
--- a/docs/reference/endpoints.html
+++ b/docs/reference/endpoints.html
@@ -18,12 +18,22 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints" />
 
+<meta property="og:description" content="This function calculates DT50 and DT90 values as well as formation fractions from kinetic models
+fitted with mkinfit. If the SFORB model was specified for one of the parents or metabolites,
+the Eigenvalues are returned. These are equivalent to the rate constantes of the DFOP model, but
+with the advantage that the SFORB model can also be used for metabolites." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +96,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -171,7 +176,7 @@ with the advantage that the SFORB model can also be used for metabolites.</p>
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/geometric_mean.html b/docs/reference/geometric_mean.html
index 4af50f48..2d46b4de 100644
--- a/docs/reference/geometric_mean.html
+++ b/docs/reference/geometric_mean.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Calculate the geometric mean — geometric_mean" />
 
+<meta property="og:description" content="Function calculating the geometric mean of numeric vectors" />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -152,7 +154,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html
index 6c3979c5..2c51001f 100644
--- a/docs/reference/ilr.html
+++ b/docs/reference/ilr.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Function to perform isometric log-ratio transformation — ilr" />
 
+<meta property="og:description" content="This implementation is a special case of the class of isometric log-ratio transformations." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -176,7 +178,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 4675748c..218194ad 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -18,12 +18,16 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
-
+  
+  
+<meta property="og:title" content="Function reference" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -442,7 +446,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/max_twa_parent.html b/docs/reference/max_twa_parent.html
index 0e99e579..5d6baf6a 100644
--- a/docs/reference/max_twa_parent.html
+++ b/docs/reference/max_twa_parent.html
@@ -18,12 +18,23 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent" />
 
+<meta property="og:description" content="This function calculates maximum moving window time weighted average concentrations
+(TWAs) for kinetic models fitted with mkinfit. Currently, only
+calculations for the parent are implemented for the SFO, FOMC and DFOP models,
+using the analytical formulas given in the PEC soil section of the FOCUS
+guidance." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +97,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -139,7 +145,7 @@ guidance.</p>
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
@@ -172,7 +178,7 @@ guidance.</p>
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html
index 0111deb3..ec7f8ccd 100644
--- a/docs/reference/mccall81_245T.html
+++ b/docs/reference/mccall81_245T.html
@@ -18,12 +18,21 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T" />
 
+<meta property="og:description" content="Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding
+ 2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether
+ extracts." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +79,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +95,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -127,108 +134,25 @@
     <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
 
     <p>McCall P, Vrona SA, Kelley SS (1981) Fate of uniformly carbon-14 ring labeled 2,4,5-Trichlorophenoxyacetic acid and 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107
-  <a href = 'http://dx.doi.org/10.1021/jf00103a026'>http://dx.doi.org/10.1021/jf00103a026</a></p>
+  <a href='http://dx.doi.org/10.1021/jf00103a026'>http://dx.doi.org/10.1021/jf00103a026</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'>  <span class='no'>SFO_SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>T245</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"phenol"</span>),
                          <span class='kw'>phenol</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"anisole"</span>),
-                         <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+                         <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>  </div><span class='co'># NOT RUN {</span>
     <span class='no'>fit.1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'>subset</span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-    <span class='fu'>summary</span>(<span class='no'>fit.1</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version:    0.9.46 
-#&gt; R version:       3.4.1 
-#&gt; Date of fit:     Sat Jul 29 15:14:14 2017 
-#&gt; Date of summary: Sat Jul 29 15:14:14 2017 
-#&gt; 
-#&gt; Equations:
-#&gt; d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245
-#&gt; d_phenol/dt = + k_T245_phenol * T245 - k_phenol_sink * phenol -
-#&gt;            k_phenol_anisole * phenol
-#&gt; d_anisole/dt = + k_phenol_anisole * phenol - k_anisole_sink * anisole
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 612 model solutions performed in 3.558 s
-#&gt; 
-#&gt; Weighting: none
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                     value   type
-#&gt; T245_0           100.9000  state
-#&gt; k_T245_sink        0.1000 deparm
-#&gt; k_T245_phenol      0.1001 deparm
-#&gt; k_phenol_sink      0.1002 deparm
-#&gt; k_phenol_anisole   0.1003 deparm
-#&gt; k_anisole_sink     0.1004 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                           value lower upper
-#&gt; T245_0               100.900000  -Inf   Inf
-#&gt; log_k_T245_sink       -2.302585  -Inf   Inf
-#&gt; log_k_T245_phenol     -2.301586  -Inf   Inf
-#&gt; log_k_phenol_sink     -2.300587  -Inf   Inf
-#&gt; log_k_phenol_anisole  -2.299590  -Inf   Inf
-#&gt; log_k_anisole_sink    -2.298593  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;           value  type
-#&gt; phenol_0      0 state
-#&gt; anisole_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                      Estimate Std. Error Lower Upper
-#&gt; T245_0               103.9000         NA    NA    NA
-#&gt; log_k_T245_sink       -4.1130         NA    NA    NA
-#&gt; log_k_T245_phenol     -3.6120         NA    NA    NA
-#&gt; log_k_phenol_sink    -26.8400         NA    NA    NA
-#&gt; log_k_phenol_anisole  -0.9037         NA    NA    NA
-#&gt; log_k_anisole_sink    -5.0090         NA    NA    NA
-#&gt; 
-#&gt; Parameter correlation:</div><div class='output co'>#&gt; <span class='warning'>Warning: Could not estimate covariance matrix; singular system:</span></div><div class='output co'>#&gt; Could not estimate covariance matrix; singular system:
-#&gt; 
-#&gt; Residual standard error: 2.78 on 18 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                   Estimate   t value    Pr(&gt;t) Lower Upper
-#&gt; T245_0           1.039e+02 4.282e+01 7.236e-20    NA    NA
-#&gt; k_T245_sink      1.636e-02 8.901e-01 1.926e-01    NA    NA
-#&gt; k_T245_phenol    2.701e-02 1.504e+00 7.499e-02    NA    NA
-#&gt; k_phenol_sink    2.212e-12 7.870e-12 5.000e-01    NA    NA
-#&gt; k_phenol_anisole 4.051e-01 2.518e+00 1.075e-02    NA    NA
-#&gt; k_anisole_sink   6.679e-03 8.146e+00 9.469e-08    NA    NA
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data  10.070       6 16
-#&gt; T245       7.908       3  5
-#&gt; phenol   106.445       2  5
-#&gt; anisole    5.379       1  6
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                       ff
-#&gt; T245_sink      3.772e-01
-#&gt; T245_phenol    6.228e-01
-#&gt; phenol_sink    5.462e-12
-#&gt; phenol_anisole 1.000e+00
-#&gt; anisole_sink   1.000e+00
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;            DT50    DT90
-#&gt; T245     15.982  53.091
-#&gt; phenol    1.711   5.685
-#&gt; anisole 103.784 344.763</div><div class='input'>
-  <span class='co'># No convergence, no covariance matrix ...</span>
+    <span class='fu'>summary</span>(<span class='no'>fit.1</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
+
+<span class='co'># }</span><div class='input'>  <span class='co'># No convergence, no covariance matrix ...</span>
   <span class='co'># k_phenol_sink is really small, therefore fix it to zero</span>
   <span class='no'>fit.2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'>subset</span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>),
                  <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='kw'>k_phenol_sink</span> <span class='kw'>=</span> <span class='fl'>0</span>),
                  <span class='kw'>fixed_parms</span> <span class='kw'>=</span> <span class='st'>"k_phenol_sink"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-  <span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version:    0.9.46 
-#&gt; R version:       3.4.1 
-#&gt; Date of fit:     Sat Jul 29 15:14:15 2017 
-#&gt; Date of summary: Sat Jul 29 15:14:15 2017 
+  <span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.47.1 
+#&gt; R version used for fitting:       3.4.3 
+#&gt; Date of fit:     Thu Mar  1 14:26:15 2018 
+#&gt; Date of summary: Thu Mar  1 14:26:15 2018 
 #&gt; 
 #&gt; Equations:
 #&gt; d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245
@@ -238,7 +162,7 @@
 #&gt; 
 #&gt; Model predictions using solution type deSolve 
 #&gt; 
-#&gt; Fitted with method Port using 246 model solutions performed in 1.431 s
+#&gt; Fitted with method Port using 246 model solutions performed in 1.359 s
 #&gt; 
 #&gt; Weighting: none
 #&gt; 
@@ -340,7 +264,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkin_long_to_wide.html b/docs/reference/mkin_long_to_wide.html
index 042bdced..a5432dac 100644
--- a/docs/reference/mkin_long_to_wide.html
+++ b/docs/reference/mkin_long_to_wide.html
@@ -18,12 +18,21 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Convert a dataframe from long to wide format — mkin_long_to_wide" />
 
+<meta property="og:description" content="This function takes a dataframe in the long form as required by modCost
+  and converts it into a dataframe with one independent variable and several
+  dependent variables as columns." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +95,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -186,7 +190,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html
index ddf28dd9..6798efa6 100644
--- a/docs/reference/mkin_wide_to_long.html
+++ b/docs/reference/mkin_wide_to_long.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Convert a dataframe with observations over time into long format — mkin_wide_to_long" />
 
+<meta property="og:description" content="This function simply takes a dataframe with one independent variable and several
+  dependent variable and converts it into the long form as required by modCost." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +94,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -165,7 +168,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html
index 2d8d64a5..ab06e903 100644
--- a/docs/reference/mkinds.html
+++ b/docs/reference/mkinds.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="A dataset class for mkin — mkinds" />
 
+<meta property="og:description" content="A dataset class for mkin" />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -154,7 +156,7 @@ in order to be compatible with mkinfit</p></dd>
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html
index 67b7a35e..496cce15 100644
--- a/docs/reference/mkinerrmin.html
+++ b/docs/reference/mkinerrmin.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Calculate the minimum error to assume in order to pass the variance test — mkinerrmin" />
 
+<meta property="og:description" content="This function finds the smallest relative error still resulting in passing the
+chi-squared test as defined in the FOCUS kinetics report from 2006." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +78,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +94,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -146,7 +152,7 @@ chi-squared test as defined in the FOCUS kinetics report from 2006.</p>
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
@@ -186,7 +192,7 @@ chi-squared test as defined in the FOCUS kinetics report from 2006.</p>
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index 0102aecb..4fb5ef9a 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -18,12 +18,29 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Fit a kinetic model to data with one or more state variables — mkinfit" />
 
+<meta property="og:description" content="This function uses the Flexible Modelling Environment package
+  FME to create a function calculating the model cost, i.e. the
+  deviation between the kinetic model and the observed data. This model cost is
+  then minimised using the Port algorithm nlminb,
+  using the specified initial or fixed parameters and starting values.
+  Per default, parameters in the kinetic models are internally transformed in order
+  to better satisfy the assumption of a normal distribution of their estimators.
+  In each step of the optimsation, the kinetic model is solved using the
+  function mkinpredict. The variance of the residuals for each
+  observed variable can optionally be iteratively reweighted until convergence
+  using the argument reweight.method = "obs"." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -395,17 +412,17 @@
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'><span class='co'># Use shorthand notation for parent only degradation</span>
 <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; mkin version:    0.9.47.1 
-#&gt; R version:       3.4.3 
-#&gt; Date of fit:     Tue Jan 30 10:05:48 2018 
-#&gt; Date of summary: Tue Jan 30 10:05:48 2018 
+<span class='fu'>summary</span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.47.1 
+#&gt; R version used for fitting:       3.4.3 
+#&gt; Date of fit:     Thu Mar  1 14:26:18 2018 
+#&gt; Date of summary: Thu Mar  1 14:26:18 2018 
 #&gt; 
 #&gt; Equations:
 #&gt; d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 #&gt; 
 #&gt; Model predictions using solution type analytical 
 #&gt; 
-#&gt; Fitted with method Port using 64 model solutions performed in 0.31 s
+#&gt; Fitted with method Port using 64 model solutions performed in 0.135 s
 #&gt; 
 #&gt; Weighting: none
 #&gt; 
@@ -474,7 +491,7 @@
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
 <span class='fu'>print</span>(<span class='fu'>system.time</span>(<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>,
                            <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)))</div><div class='output co'>#&gt;    user  system elapsed 
-#&gt;   1.196   0.000   1.195 </div><div class='input'><span class='fu'>coef</span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt;          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
+#&gt;    0.84    0.00    0.84 </div><div class='input'><span class='fu'>coef</span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt;          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
 #&gt;          99.59848          -3.03822          -2.98030          -5.24750 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; $ff
 #&gt; parent_sink   parent_m1     m1_sink 
 #&gt;    0.485524    0.514476    1.000000 
@@ -486,92 +503,19 @@
 #&gt;              DT50      DT90
 #&gt; parent   7.022929  23.32967
 #&gt; m1     131.760712 437.69961
-#&gt; </div><div class='input'>
+#&gt; </div><span class='co'># NOT RUN {</span>
 <span class='co'># deSolve is slower when no C compiler (gcc) was available during model generation</span>
 <span class='fu'>print</span>(<span class='fu'>system.time</span>(<span class='no'>fit.deSolve</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>,
-                           <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)))</div><div class='output co'>#&gt; Model cost at call  1 :  18915.53 
-#&gt; Model cost at call  2 :  18915.53 
-#&gt; Model cost at call  6 :  11424.02 
-#&gt; Model cost at call  10 :  11424 
-#&gt; Model cost at call  12 :  4094.396 
-#&gt; Model cost at call  16 :  4094.396 
-#&gt; Model cost at call  19 :  1340.595 
-#&gt; Model cost at call  20 :  1340.593 
-#&gt; Model cost at call  25 :  1072.239 
-#&gt; Model cost at call  28 :  1072.236 
-#&gt; Model cost at call  30 :  874.2614 
-#&gt; Model cost at call  33 :  874.2611 
-#&gt; Model cost at call  35 :  616.2379 
-#&gt; Model cost at call  37 :  616.2374 
-#&gt; Model cost at call  40 :  467.4387 
-#&gt; Model cost at call  42 :  467.4382 
-#&gt; Model cost at call  46 :  398.2913 
-#&gt; Model cost at call  48 :  398.2912 
-#&gt; Model cost at call  49 :  398.2911 
-#&gt; Model cost at call  51 :  395.0711 
-#&gt; Model cost at call  54 :  395.071 
-#&gt; Model cost at call  56 :  378.3298 
-#&gt; Model cost at call  59 :  378.3298 
-#&gt; Model cost at call  62 :  376.9812 
-#&gt; Model cost at call  64 :  376.9811 
-#&gt; Model cost at call  67 :  375.2085 
-#&gt; Model cost at call  69 :  375.2085 
-#&gt; Model cost at call  70 :  375.2085 
-#&gt; Model cost at call  71 :  375.2085 
-#&gt; Model cost at call  72 :  374.5723 
-#&gt; Model cost at call  74 :  374.5723 
-#&gt; Model cost at call  77 :  374.0075 
-#&gt; Model cost at call  79 :  374.0075 
-#&gt; Model cost at call  80 :  374.0075 
-#&gt; Model cost at call  82 :  373.1711 
-#&gt; Model cost at call  84 :  373.1711 
-#&gt; Model cost at call  87 :  372.6445 
-#&gt; Model cost at call  88 :  372.1614 
-#&gt; Model cost at call  90 :  372.1614 
-#&gt; Model cost at call  91 :  372.1614 
-#&gt; Model cost at call  94 :  371.6464 
-#&gt; Model cost at call  99 :  371.4299 
-#&gt; Model cost at call  101 :  371.4299 
-#&gt; Model cost at call  104 :  371.4071 
-#&gt; Model cost at call  106 :  371.4071 
-#&gt; Model cost at call  107 :  371.4071 
-#&gt; Model cost at call  109 :  371.2524 
-#&gt; Model cost at call  113 :  371.2524 
-#&gt; Model cost at call  114 :  371.2136 
-#&gt; Model cost at call  115 :  371.2136 
-#&gt; Model cost at call  116 :  371.2136 
-#&gt; Model cost at call  119 :  371.2134 
-#&gt; Model cost at call  120 :  371.2134 
-#&gt; Model cost at call  122 :  371.2134 
-#&gt; Model cost at call  123 :  371.2134 
-#&gt; Model cost at call  125 :  371.2134 
-#&gt; Model cost at call  126 :  371.2134 
-#&gt; Model cost at call  135 :  371.2134 
-#&gt; Model cost at call  147 :  371.2134 
-#&gt; Model cost at call  151 :  371.2134 
-#&gt; Model cost at call  152 :  371.2134 
-#&gt; Model cost at call  153 :  371.2134 
-#&gt; Optimisation by method Port successfully terminated.
-#&gt;    user  system elapsed 
-#&gt;   1.008   0.000   1.006 </div><div class='input'><span class='fu'>coef</span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt;          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
-#&gt;          99.59848          -3.03822          -2.98030          -5.24750 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; $ff
-#&gt; parent_sink   parent_m1     m1_sink 
-#&gt;    0.485524    0.514476    1.000000 
-#&gt; 
-#&gt; $SFORB
-#&gt; logical(0)
-#&gt; 
-#&gt; $distimes
-#&gt;              DT50      DT90
-#&gt; parent   7.022929  23.32967
-#&gt; m1     131.760712 437.69961
-#&gt; </div><div class='input'>
-
-<span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, FOMC</span>
-
+                           <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)))
+<span class='fu'>coef</span>(<span class='no'>fit.deSolve</span>)
+<span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)
+<span class='co'># }</span><div class='input'>
+# Use stepwise fitting, using optimised parameters from parent only fit, FOMC
+</div><span class='co'># NOT RUN {</span>
 <span class='no'>FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"FOMC"</span>, <span class='st'>"m1"</span>),
-  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
+  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))
+<span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
 <span class='no'>fit.FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
 <span class='co'># Use starting parameters from parent only FOMC fit</span>
 <span class='no'>fit.FOMC</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
@@ -581,739 +525,40 @@
 <span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, SFORB</span>
 <span class='no'>SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFORB"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
-  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
+  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))
+<span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
 <span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
 <span class='no'>fit.SFORB_SFO.deSolve</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>,
                                  <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
 <span class='co'># Use starting parameters from parent only SFORB fit (not really needed in this case)</span>
 <span class='no'>fit.SFORB</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"SFORB"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
 <span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<span class='no'>bparms.ode</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-
-
-
+<span class='co'># }</span><div class='input'>
+</div><span class='co'># NOT RUN {</span>
 <span class='co'># Weighted fits, including IRLS</span>
 <span class='no'>SFO_SFO.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
-                      <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f.noweight</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#&gt; mkin version:    0.9.47.1 
-#&gt; R version:       3.4.3 
-#&gt; Date of fit:     Tue Jan 30 10:06:00 2018 
-#&gt; Date of summary: Tue Jan 30 10:06:00 2018 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 185 model solutions performed in 0.739 s
-#&gt; 
-#&gt; Weighting: none
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                   value   type
-#&gt; parent_0       100.7500  state
-#&gt; k_parent         0.1000 deparm
-#&gt; k_m1             0.1001 deparm
-#&gt; f_parent_to_m1   0.5000 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                     value lower upper
-#&gt; parent_0       100.750000  -Inf   Inf
-#&gt; log_k_parent    -2.302585  -Inf   Inf
-#&gt; log_k_m1        -2.301586  -Inf   Inf
-#&gt; f_parent_ilr_1   0.000000  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                Estimate Std. Error   Lower    Upper
-#&gt; parent_0       99.60000    1.61400 96.3300 102.9000
-#&gt; log_k_parent   -2.31600    0.04187 -2.4010  -2.2310
-#&gt; log_k_m1       -5.24800    0.13610 -5.5230  -4.9720
-#&gt; f_parent_ilr_1  0.04096    0.06477 -0.0904   0.1723
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#&gt; parent_0         1.0000       0.5178  -0.1701        -0.5489
-#&gt; log_k_parent     0.5178       1.0000  -0.3285        -0.5451
-#&gt; log_k_m1        -0.1701      -0.3285   1.0000         0.7466
-#&gt; f_parent_ilr_1  -0.5489      -0.5451   0.7466         1.0000
-#&gt; 
-#&gt; Residual standard error: 3.211 on 36 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                 Estimate t value    Pr(&gt;t)     Lower     Upper
-#&gt; parent_0       99.600000  61.720 2.024e-38 96.330000 1.029e+02
-#&gt; k_parent        0.098700  23.880 5.701e-24  0.090660 1.074e-01
-#&gt; k_m1            0.005261   7.349 5.758e-09  0.003992 6.933e-03
-#&gt; f_parent_to_m1  0.514500  22.490 4.374e-23  0.468100 5.606e-01
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   6.398       4 15
-#&gt; parent     6.459       2  7
-#&gt; m1         4.690       2  8
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                 ff
-#&gt; parent_m1   0.5145
-#&gt; parent_sink 0.4855
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;           DT50   DT90
-#&gt; parent   7.023  23.33
-#&gt; m1     131.761 437.70
-#&gt; 
-#&gt; Data:
-#&gt;  time variable observed predicted   residual
-#&gt;     0   parent    99.46  99.59848 -1.385e-01
-#&gt;     0   parent   102.04  99.59848  2.442e+00
-#&gt;     1   parent    93.50  90.23787  3.262e+00
-#&gt;     1   parent    92.50  90.23787  2.262e+00
-#&gt;     3   parent    63.23  74.07319 -1.084e+01
-#&gt;     3   parent    68.99  74.07319 -5.083e+00
-#&gt;     7   parent    52.32  49.91206  2.408e+00
-#&gt;     7   parent    55.13  49.91206  5.218e+00
-#&gt;    14   parent    27.27  25.01257  2.257e+00
-#&gt;    14   parent    26.64  25.01257  1.627e+00
-#&gt;    21   parent    11.50  12.53462 -1.035e+00
-#&gt;    21   parent    11.64  12.53462 -8.946e-01
-#&gt;    35   parent     2.85   3.14787 -2.979e-01
-#&gt;    35   parent     2.91   3.14787 -2.379e-01
-#&gt;    50   parent     0.69   0.71624 -2.624e-02
-#&gt;    50   parent     0.63   0.71624 -8.624e-02
-#&gt;    75   parent     0.05   0.06074 -1.074e-02
-#&gt;    75   parent     0.06   0.06074 -7.381e-04
-#&gt;     0       m1     0.00   0.00000  0.000e+00
-#&gt;     0       m1     0.00   0.00000  0.000e+00
-#&gt;     1       m1     4.84   4.80296  3.704e-02
-#&gt;     1       m1     5.64   4.80296  8.370e-01
-#&gt;     3       m1    12.91  13.02400 -1.140e-01
-#&gt;     3       m1    12.96  13.02400 -6.400e-02
-#&gt;     7       m1    22.97  25.04476 -2.075e+00
-#&gt;     7       m1    24.47  25.04476 -5.748e-01
-#&gt;    14       m1    41.69  36.69002  5.000e+00
-#&gt;    14       m1    33.21  36.69002 -3.480e+00
-#&gt;    21       m1    44.37  41.65310  2.717e+00
-#&gt;    21       m1    46.44  41.65310  4.787e+00
-#&gt;    35       m1    41.22  43.31312 -2.093e+00
-#&gt;    35       m1    37.95  43.31312 -5.363e+00
-#&gt;    50       m1    41.19  41.21831 -2.831e-02
-#&gt;    50       m1    40.01  41.21831 -1.208e+00
-#&gt;    75       m1    40.09  36.44703  3.643e+00
-#&gt;    75       m1    33.85  36.44703 -2.597e+00
-#&gt;   100       m1    31.04  31.98163 -9.416e-01
-#&gt;   100       m1    33.13  31.98163  1.148e+00
-#&gt;   120       m1    25.15  28.78984 -3.640e+00
-#&gt;   120       m1    33.31  28.78984  4.520e+00</div><div class='input'><span class='no'>f.irls</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>f.irls</span>)</div><div class='output co'>#&gt; mkin version:    0.9.47.1 
-#&gt; R version:       3.4.3 
-#&gt; Date of fit:     Tue Jan 30 10:06:02 2018 
-#&gt; Date of summary: Tue Jan 30 10:06:02 2018 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 523 model solutions performed in 2.151 s
-#&gt; 
-#&gt; Weighting: none
-#&gt; 
-#&gt; Iterative reweighting with method obs 
-#&gt; Final mean squared residuals of observed variables:
-#&gt;    parent        m1 
-#&gt; 11.573408  7.407845 
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                   value   type
-#&gt; parent_0       100.7500  state
-#&gt; k_parent         0.1000 deparm
-#&gt; k_m1             0.1001 deparm
-#&gt; f_parent_to_m1   0.5000 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                     value lower upper
-#&gt; parent_0       100.750000  -Inf   Inf
-#&gt; log_k_parent    -2.302585  -Inf   Inf
-#&gt; log_k_m1        -2.301586  -Inf   Inf
-#&gt; f_parent_ilr_1   0.000000  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                Estimate Std. Error    Lower   Upper
-#&gt; parent_0       99.67000    1.79200 96.04000 103.300
-#&gt; log_k_parent   -2.31200    0.04560 -2.40400  -2.219
-#&gt; log_k_m1       -5.25100    0.12510 -5.50500  -4.998
-#&gt; f_parent_ilr_1  0.03785    0.06318 -0.09027   0.166
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#&gt; parent_0         1.0000       0.5083  -0.1979        -0.6148
-#&gt; log_k_parent     0.5083       1.0000  -0.3894        -0.6062
-#&gt; log_k_m1        -0.1979      -0.3894   1.0000         0.7417
-#&gt; f_parent_ilr_1  -0.6148      -0.6062   0.7417         1.0000
-#&gt; 
-#&gt; Residual standard error: 1.054 on 36 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                Estimate t value    Pr(&gt;t)     Lower     Upper
-#&gt; parent_0       99.67000  55.630 8.184e-37 96.040000 1.033e+02
-#&gt; k_parent        0.09906  21.930 1.016e-22  0.090310 1.087e-01
-#&gt; k_m1            0.00524   7.996 8.486e-10  0.004066 6.753e-03
-#&gt; f_parent_to_m1  0.51340  23.000 2.038e-23  0.468100 5.584e-01
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   6.399       4 15
-#&gt; parent     6.466       2  7
-#&gt; m1         4.679       2  8
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                 ff
-#&gt; parent_m1   0.5134
-#&gt; parent_sink 0.4866
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;           DT50   DT90
-#&gt; parent   6.997  23.24
-#&gt; m1     132.282 439.43
-#&gt; 
-#&gt; Data:
-#&gt;  time variable observed predicted   residual   err
-#&gt;     0   parent    99.46  99.67218 -2.122e-01 3.402
-#&gt;     0   parent   102.04  99.67218  2.368e+00 3.402
-#&gt;     1   parent    93.50  90.27153  3.228e+00 3.402
-#&gt;     1   parent    92.50  90.27153  2.228e+00 3.402
-#&gt;     3   parent    63.23  74.04648 -1.082e+01 3.402
-#&gt;     3   parent    68.99  74.04648 -5.056e+00 3.402
-#&gt;     7   parent    52.32  49.82092  2.499e+00 3.402
-#&gt;     7   parent    55.13  49.82092  5.309e+00 3.402
-#&gt;    14   parent    27.27  24.90287  2.367e+00 3.402
-#&gt;    14   parent    26.64  24.90287  1.737e+00 3.402
-#&gt;    21   parent    11.50  12.44764 -9.476e-01 3.402
-#&gt;    21   parent    11.64  12.44764 -8.076e-01 3.402
-#&gt;    35   parent     2.85   3.11002 -2.600e-01 3.402
-#&gt;    35   parent     2.91   3.11002 -2.000e-01 3.402
-#&gt;    50   parent     0.69   0.70374 -1.374e-02 3.402
-#&gt;    50   parent     0.63   0.70374 -7.374e-02 3.402
-#&gt;    75   parent     0.05   0.05913 -9.134e-03 3.402
-#&gt;    75   parent     0.06   0.05913  8.662e-04 3.402
-#&gt;     0       m1     0.00   0.00000  0.000e+00 2.722
-#&gt;     0       m1     0.00   0.00000  0.000e+00 2.722
-#&gt;     1       m1     4.84   4.81328  2.672e-02 2.722
-#&gt;     1       m1     5.64   4.81328  8.267e-01 2.722
-#&gt;     3       m1    12.91  13.04779 -1.378e-01 2.722
-#&gt;     3       m1    12.96  13.04779 -8.779e-02 2.722
-#&gt;     7       m1    22.97  25.07615 -2.106e+00 2.722
-#&gt;     7       m1    24.47  25.07615 -6.062e-01 2.722
-#&gt;    14       m1    41.69  36.70729  4.983e+00 2.722
-#&gt;    14       m1    33.21  36.70729 -3.497e+00 2.722
-#&gt;    21       m1    44.37  41.65050  2.720e+00 2.722
-#&gt;    21       m1    46.44  41.65050  4.790e+00 2.722
-#&gt;    35       m1    41.22  43.28866 -2.069e+00 2.722
-#&gt;    35       m1    37.95  43.28866 -5.339e+00 2.722
-#&gt;    50       m1    41.19  41.19338 -3.383e-03 2.722
-#&gt;    50       m1    40.01  41.19338 -1.183e+00 2.722
-#&gt;    75       m1    40.09  36.43820  3.652e+00 2.722
-#&gt;    75       m1    33.85  36.43820 -2.588e+00 2.722
-#&gt;   100       m1    31.04  31.98971 -9.497e-01 2.722
-#&gt;   100       m1    33.13  31.98971  1.140e+00 2.722
-#&gt;   120       m1    25.15  28.80897 -3.659e+00 2.722
-#&gt;   120       m1    33.31  28.80897  4.501e+00 2.722</div><div class='input'><span class='no'>f.w.mean</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>weight</span> <span class='kw'>=</span> <span class='st'>"mean"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>f.w.mean</span>)</div><div class='output co'>#&gt; mkin version:    0.9.47.1 
-#&gt; R version:       3.4.3 
-#&gt; Date of fit:     Tue Jan 30 10:06:03 2018 
-#&gt; Date of summary: Tue Jan 30 10:06:03 2018 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 155 model solutions performed in 0.675 s
-#&gt; 
-#&gt; Weighting: mean
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                   value   type
-#&gt; parent_0       100.7500  state
-#&gt; k_parent         0.1000 deparm
-#&gt; k_m1             0.1001 deparm
-#&gt; f_parent_to_m1   0.5000 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                     value lower upper
-#&gt; parent_0       100.750000  -Inf   Inf
-#&gt; log_k_parent    -2.302585  -Inf   Inf
-#&gt; log_k_m1        -2.301586  -Inf   Inf
-#&gt; f_parent_ilr_1   0.000000  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                Estimate Std. Error    Lower    Upper
-#&gt; parent_0        99.7300    1.93200 95.81000 103.6000
-#&gt; log_k_parent    -2.3090    0.04837 -2.40700  -2.2110
-#&gt; log_k_m1        -5.2550    0.12070 -5.49900  -5.0100
-#&gt; f_parent_ilr_1   0.0354    0.06344 -0.09327   0.1641
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#&gt; parent_0         1.0000       0.5004  -0.2143        -0.6514
-#&gt; log_k_parent     0.5004       1.0000  -0.4282        -0.6383
-#&gt; log_k_m1        -0.2143      -0.4282   1.0000         0.7390
-#&gt; f_parent_ilr_1  -0.6514      -0.6383   0.7390         1.0000
-#&gt; 
-#&gt; Residual standard error: 0.09829 on 36 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                 Estimate t value    Pr(&gt;t)    Lower     Upper
-#&gt; parent_0       99.730000  51.630 1.166e-35 95.81000 1.036e+02
-#&gt; k_parent        0.099360  20.670 7.303e-22  0.09007 1.096e-01
-#&gt; k_m1            0.005224   8.287 3.649e-10  0.00409 6.672e-03
-#&gt; f_parent_to_m1  0.512500  22.860 2.497e-23  0.46710 5.578e-01
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   6.401       4 15
-#&gt; parent     6.473       2  7
-#&gt; m1         4.671       2  8
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                 ff
-#&gt; parent_m1   0.5125
-#&gt; parent_sink 0.4875
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;           DT50   DT90
-#&gt; parent   6.976  23.18
-#&gt; m1     132.696 440.81
-#&gt; 
-#&gt; Data:
-#&gt;  time variable observed predicted   residual
-#&gt;     0   parent    99.46  99.73057  -0.270570
-#&gt;     0   parent   102.04  99.73057   2.309430
-#&gt;     1   parent    93.50  90.29805   3.201945
-#&gt;     1   parent    92.50  90.29805   2.201945
-#&gt;     3   parent    63.23  74.02503 -10.795028
-#&gt;     3   parent    68.99  74.02503  -5.035028
-#&gt;     7   parent    52.32  49.74838   2.571618
-#&gt;     7   parent    55.13  49.74838   5.381618
-#&gt;    14   parent    27.27  24.81588   2.454124
-#&gt;    14   parent    26.64  24.81588   1.824124
-#&gt;    21   parent    11.50  12.37885  -0.878849
-#&gt;    21   parent    11.64  12.37885  -0.738849
-#&gt;    35   parent     2.85   3.08022  -0.230219
-#&gt;    35   parent     2.91   3.08022  -0.170219
-#&gt;    50   parent     0.69   0.69396  -0.003958
-#&gt;    50   parent     0.63   0.69396  -0.063958
-#&gt;    75   parent     0.05   0.05789  -0.007888
-#&gt;    75   parent     0.06   0.05789   0.002112
-#&gt;     0       m1     0.00   0.00000   0.000000
-#&gt;     0       m1     0.00   0.00000   0.000000
-#&gt;     1       m1     4.84   4.82149   0.018512
-#&gt;     1       m1     5.64   4.82149   0.818512
-#&gt;     3       m1    12.91  13.06669  -0.156692
-#&gt;     3       m1    12.96  13.06669  -0.106692
-#&gt;     7       m1    22.97  25.10106  -2.131058
-#&gt;     7       m1    24.47  25.10106  -0.631058
-#&gt;    14       m1    41.69  36.72092   4.969077
-#&gt;    14       m1    33.21  36.72092  -3.510923
-#&gt;    21       m1    44.37  41.64835   2.721647
-#&gt;    21       m1    46.44  41.64835   4.791647
-#&gt;    35       m1    41.22  43.26923  -2.049225
-#&gt;    35       m1    37.95  43.26923  -5.319225
-#&gt;    50       m1    41.19  41.17364   0.016361
-#&gt;    50       m1    40.01  41.17364  -1.163639
-#&gt;    75       m1    40.09  36.43122   3.658776
-#&gt;    75       m1    33.85  36.43122  -2.581224
-#&gt;   100       m1    31.04  31.99612  -0.956124
-#&gt;   100       m1    33.13  31.99612   1.133876
-#&gt;   120       m1    25.15  28.82413  -3.674128
-#&gt;   120       m1    33.31  28.82413   4.485872</div><div class='input'><span class='no'>f.w.value</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'>subset</span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"value"</span>,
+                      <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)
+<span class='no'>f.noweight</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'>summary</span>(<span class='no'>f.noweight</span>)
+<span class='no'>f.irls</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'>summary</span>(<span class='no'>f.irls</span>)
+<span class='no'>f.w.mean</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>weight</span> <span class='kw'>=</span> <span class='st'>"mean"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'>summary</span>(<span class='no'>f.w.mean</span>)
+<span class='no'>f.w.value</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'>subset</span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"value"</span>,
                      <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>f.w.value</span>)</div><div class='output co'>#&gt; mkin version:    0.9.47.1 
-#&gt; R version:       3.4.3 
-#&gt; Date of fit:     Tue Jan 30 10:06:04 2018 
-#&gt; Date of summary: Tue Jan 30 10:06:04 2018 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 174 model solutions performed in 0.68 s
-#&gt; 
-#&gt; Weighting: manual
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                   value   type
-#&gt; parent_0       100.7500  state
-#&gt; k_parent         0.1000 deparm
-#&gt; k_m1             0.1001 deparm
-#&gt; f_parent_to_m1   0.5000 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                     value lower upper
-#&gt; parent_0       100.750000  -Inf   Inf
-#&gt; log_k_parent    -2.302585  -Inf   Inf
-#&gt; log_k_m1        -2.301586  -Inf   Inf
-#&gt; f_parent_ilr_1   0.000000  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                Estimate Std. Error    Lower    Upper
-#&gt; parent_0        99.6600   2.712000 94.14000 105.2000
-#&gt; log_k_parent    -2.2980   0.008118 -2.31500  -2.2820
-#&gt; log_k_m1        -5.2410   0.096690 -5.43800  -5.0450
-#&gt; f_parent_ilr_1   0.0231   0.057990 -0.09474   0.1409
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#&gt; parent_0        1.00000       0.6844 -0.08687        -0.7564
-#&gt; log_k_parent    0.68435       1.0000 -0.12694        -0.5812
-#&gt; log_k_m1       -0.08687      -0.1269  1.00000         0.5195
-#&gt; f_parent_ilr_1 -0.75644      -0.5812  0.51951         1.0000
-#&gt; 
-#&gt; Residual standard error: 0.08396 on 34 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                 Estimate t value    Pr(&gt;t)    Lower     Upper
-#&gt; parent_0       99.660000   36.75 2.957e-29 94.14000 1.052e+02
-#&gt; k_parent        0.100400  123.20 5.927e-47  0.09878 1.021e-01
-#&gt; k_m1            0.005295   10.34 2.447e-12  0.00435 6.444e-03
-#&gt; f_parent_to_m1  0.508200   24.79 1.184e-23  0.46660 5.497e-01
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   6.461       4 15
-#&gt; parent     6.520       2  7
-#&gt; m1         4.744       2  8
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                 ff
-#&gt; parent_m1   0.5082
-#&gt; parent_sink 0.4918
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;           DT50   DT90
-#&gt; parent   6.902  22.93
-#&gt; m1     130.916 434.89
-#&gt; 
-#&gt; Data:
-#&gt;  time variable observed predicted   residual    err
-#&gt;     0   parent    99.46  99.65571  -0.195714  99.46
-#&gt;     0   parent   102.04  99.65571   2.384286 102.04
-#&gt;     1   parent    93.50  90.13383   3.366170  93.50
-#&gt;     1   parent    92.50  90.13383   2.366170  92.50
-#&gt;     3   parent    63.23  73.73252 -10.502518  63.23
-#&gt;     3   parent    68.99  73.73252  -4.742518  68.99
-#&gt;     7   parent    52.32  49.34027   2.979728  52.32
-#&gt;     7   parent    55.13  49.34027   5.789728  55.13
-#&gt;    14   parent    27.27  24.42873   2.841271  27.27
-#&gt;    14   parent    26.64  24.42873   2.211271  26.64
-#&gt;    21   parent    11.50  12.09484  -0.594842  11.50
-#&gt;    21   parent    11.64  12.09484  -0.454842  11.64
-#&gt;    35   parent     2.85   2.96482  -0.114824   2.85
-#&gt;    35   parent     2.91   2.96482  -0.054824   2.91
-#&gt;    50   parent     0.69   0.65733   0.032670   0.69
-#&gt;    50   parent     0.63   0.65733  -0.027330   0.63
-#&gt;    75   parent     0.05   0.05339  -0.003386   0.05
-#&gt;    75   parent     0.06   0.05339   0.006614   0.06
-#&gt;     1       m1     4.84   4.82570   0.014301   4.84
-#&gt;     1       m1     5.64   4.82570   0.814301   5.64
-#&gt;     3       m1    12.91  13.06402  -0.154020  12.91
-#&gt;     3       m1    12.96  13.06402  -0.104020  12.96
-#&gt;     7       m1    22.97  25.04656  -2.076564  22.97
-#&gt;     7       m1    24.47  25.04656  -0.576564  24.47
-#&gt;    14       m1    41.69  36.53601   5.153988  41.69
-#&gt;    14       m1    33.21  36.53601  -3.326012  33.21
-#&gt;    21       m1    44.37  41.34639   3.023609  44.37
-#&gt;    21       m1    46.44  41.34639   5.093609  46.44
-#&gt;    35       m1    41.22  42.82669  -1.606690  41.22
-#&gt;    35       m1    37.95  42.82669  -4.876690  37.95
-#&gt;    50       m1    41.19  40.67342   0.516578  41.19
-#&gt;    50       m1    40.01  40.67342  -0.663422  40.01
-#&gt;    75       m1    40.09  35.91105   4.178947  40.09
-#&gt;    75       m1    33.85  35.91105  -2.061053  33.85
-#&gt;   100       m1    31.04  31.48161  -0.441612  31.04
-#&gt;   100       m1    33.13  31.48161   1.648388  33.13
-#&gt;   120       m1    25.15  28.32018  -3.170181  25.15
-#&gt;   120       m1    33.31  28.32018   4.989819  33.31</div><div class='input'>
-
-
+<span class='fu'>summary</span>(<span class='no'>f.w.value</span>)
+<span class='co'># }</span><div class='input'>
+</div><span class='co'># NOT RUN {</span>
 <span class='co'># Manual weighting</span>
 <span class='no'>dw</span> <span class='kw'>&lt;-</span> <span class='no'>FOCUS_2006_D</span>
 <span class='no'>errors</span> <span class='kw'>&lt;-</span> <span class='fu'>c</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>1</span>)
 <span class='no'>dw</span>$<span class='no'>err.man</span> <span class='kw'>&lt;-</span> <span class='no'>errors</span>[<span class='no'>FOCUS_2006_D</span>$<span class='no'>name</span>]
 <span class='no'>f.w.man</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>f.w.man</span>)</div><div class='output co'>#&gt; mkin version:    0.9.47.1 
-#&gt; R version:       3.4.3 
-#&gt; Date of fit:     Tue Jan 30 10:06:05 2018 
-#&gt; Date of summary: Tue Jan 30 10:06:05 2018 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 297 model solutions performed in 1.178 s
-#&gt; 
-#&gt; Weighting: manual
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                   value   type
-#&gt; parent_0       100.7500  state
-#&gt; k_parent         0.1000 deparm
-#&gt; k_m1             0.1001 deparm
-#&gt; f_parent_to_m1   0.5000 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                     value lower upper
-#&gt; parent_0       100.750000  -Inf   Inf
-#&gt; log_k_parent    -2.302585  -Inf   Inf
-#&gt; log_k_m1        -2.301586  -Inf   Inf
-#&gt; f_parent_ilr_1   0.000000  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                Estimate Std. Error   Lower    Upper
-#&gt; parent_0       99.49000    1.33200 96.7800 102.2000
-#&gt; log_k_parent   -2.32100    0.03550 -2.3930  -2.2490
-#&gt; log_k_m1       -5.24100    0.21280 -5.6730  -4.8100
-#&gt; f_parent_ilr_1  0.04571    0.08966 -0.1361   0.2275
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#&gt; parent_0        1.00000       0.5312 -0.09456        -0.3351
-#&gt; log_k_parent    0.53123       1.0000 -0.17800        -0.3360
-#&gt; log_k_m1       -0.09456      -0.1780  1.00000         0.7616
-#&gt; f_parent_ilr_1 -0.33514      -0.3360  0.76156         1.0000
-#&gt; 
-#&gt; Residual standard error: 2.628 on 36 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                 Estimate t value    Pr(&gt;t)     Lower     Upper
-#&gt; parent_0       99.490000   74.69 2.221e-41 96.780000 1.022e+02
-#&gt; k_parent        0.098140   28.17 2.012e-26  0.091320 1.055e-01
-#&gt; k_m1            0.005292    4.70 1.873e-05  0.003437 8.148e-03
-#&gt; f_parent_to_m1  0.516200   16.30 1.686e-18  0.452000 5.798e-01
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   6.400       4 15
-#&gt; parent     6.454       2  7
-#&gt; m1         4.708       2  8
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                 ff
-#&gt; parent_m1   0.5162
-#&gt; parent_sink 0.4838
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;           DT50   DT90
-#&gt; parent   7.063  23.46
-#&gt; m1     130.971 435.08
-#&gt; 
-#&gt; Data:
-#&gt;  time variable observed predicted   residual err
-#&gt;     0   parent    99.46  99.48598  -0.025976   1
-#&gt;     0   parent   102.04  99.48598   2.554024   1
-#&gt;     1   parent    93.50  90.18612   3.313883   1
-#&gt;     1   parent    92.50  90.18612   2.313883   1
-#&gt;     3   parent    63.23  74.11316 -10.883162   1
-#&gt;     3   parent    68.99  74.11316  -5.123162   1
-#&gt;     7   parent    52.32  50.05029   2.269705   1
-#&gt;     7   parent    55.13  50.05029   5.079705   1
-#&gt;    14   parent    27.27  25.17975   2.090250   1
-#&gt;    14   parent    26.64  25.17975   1.460250   1
-#&gt;    21   parent    11.50  12.66765  -1.167654   1
-#&gt;    21   parent    11.64  12.66765  -1.027654   1
-#&gt;    35   parent     2.85   3.20616  -0.356164   1
-#&gt;    35   parent     2.91   3.20616  -0.296164   1
-#&gt;    50   parent     0.69   0.73562  -0.045619   1
-#&gt;    50   parent     0.63   0.73562  -0.105619   1
-#&gt;    75   parent     0.05   0.06326  -0.013256   1
-#&gt;    75   parent     0.06   0.06326  -0.003256   1
-#&gt;     0       m1     0.00   0.00000   0.000000   2
-#&gt;     0       m1     0.00   0.00000   0.000000   2
-#&gt;     1       m1     4.84   4.78729   0.052713   2
-#&gt;     1       m1     5.64   4.78729   0.852713   2
-#&gt;     3       m1    12.91  12.98785  -0.077848   2
-#&gt;     3       m1    12.96  12.98785  -0.027848   2
-#&gt;     7       m1    22.97  24.99695  -2.026945   2
-#&gt;     7       m1    24.47  24.99695  -0.526945   2
-#&gt;    14       m1    41.69  36.66353   5.026473   2
-#&gt;    14       m1    33.21  36.66353  -3.453527   2
-#&gt;    21       m1    44.37  41.65681   2.713187   2
-#&gt;    21       m1    46.44  41.65681   4.783187   2
-#&gt;    35       m1    41.22  43.35031  -2.130312   2
-#&gt;    35       m1    37.95  43.35031  -5.400312   2
-#&gt;    50       m1    41.19  41.25637  -0.066365   2
-#&gt;    50       m1    40.01  41.25637  -1.246365   2
-#&gt;    75       m1    40.09  36.46057   3.629433   2
-#&gt;    75       m1    33.85  36.46057  -2.610567   2
-#&gt;   100       m1    31.04  31.96929  -0.929288   2
-#&gt;   100       m1    33.13  31.96929   1.160712   2
-#&gt;   120       m1    25.15  28.76062  -3.610616   2
-#&gt;   120       m1    33.31  28.76062   4.549384   2</div><div class='input'><span class='no'>f.w.man.irls</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
+<span class='fu'>summary</span>(<span class='no'>f.w.man</span>)
+<span class='no'>f.w.man.irls</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
                        <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>)
-<span class='fu'>summary</span>(<span class='no'>f.w.man.irls</span>)</div><div class='output co'>#&gt; mkin version:    0.9.47.1 
-#&gt; R version:       3.4.3 
-#&gt; Date of fit:     Tue Jan 30 10:06:08 2018 
-#&gt; Date of summary: Tue Jan 30 10:06:08 2018 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 692 model solutions performed in 2.733 s
-#&gt; 
-#&gt; Weighting: manual
-#&gt; 
-#&gt; Iterative reweighting with method obs 
-#&gt; Final mean squared residuals of observed variables:
-#&gt;    parent        m1 
-#&gt; 11.573407  7.407845 
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                   value   type
-#&gt; parent_0       100.7500  state
-#&gt; k_parent         0.1000 deparm
-#&gt; k_m1             0.1001 deparm
-#&gt; f_parent_to_m1   0.5000 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                     value lower upper
-#&gt; parent_0       100.750000  -Inf   Inf
-#&gt; log_k_parent    -2.302585  -Inf   Inf
-#&gt; log_k_m1        -2.301586  -Inf   Inf
-#&gt; f_parent_ilr_1   0.000000  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                Estimate Std. Error    Lower   Upper
-#&gt; parent_0       99.67000    1.79200 96.04000 103.300
-#&gt; log_k_parent   -2.31200    0.04560 -2.40400  -2.220
-#&gt; log_k_m1       -5.25100    0.12510 -5.50500  -4.998
-#&gt; f_parent_ilr_1  0.03785    0.06318 -0.09027   0.166
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#&gt; parent_0         1.0000       0.5083  -0.1979        -0.6148
-#&gt; log_k_parent     0.5083       1.0000  -0.3894        -0.6062
-#&gt; log_k_m1        -0.1979      -0.3894   1.0000         0.7417
-#&gt; f_parent_ilr_1  -0.6148      -0.6062   0.7417         1.0000
-#&gt; 
-#&gt; Residual standard error: 1.054 on 36 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                Estimate t value    Pr(&gt;t)     Lower     Upper
-#&gt; parent_0       99.67000  55.630 8.184e-37 96.040000 1.033e+02
-#&gt; k_parent        0.09906  21.930 1.016e-22  0.090310 1.087e-01
-#&gt; k_m1            0.00524   7.996 8.486e-10  0.004066 6.753e-03
-#&gt; f_parent_to_m1  0.51340  23.000 2.038e-23  0.468100 5.584e-01
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   6.399       4 15
-#&gt; parent     6.466       2  7
-#&gt; m1         4.679       2  8
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                 ff
-#&gt; parent_m1   0.5134
-#&gt; parent_sink 0.4866
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;           DT50   DT90
-#&gt; parent   6.997  23.24
-#&gt; m1     132.282 439.43
-#&gt; 
-#&gt; Data:
-#&gt;  time variable observed predicted   residual err.ini   err
-#&gt;     0   parent    99.46  99.67218 -2.122e-01       1 3.402
-#&gt;     0   parent   102.04  99.67218  2.368e+00       1 3.402
-#&gt;     1   parent    93.50  90.27153  3.228e+00       1 3.402
-#&gt;     1   parent    92.50  90.27153  2.228e+00       1 3.402
-#&gt;     3   parent    63.23  74.04648 -1.082e+01       1 3.402
-#&gt;     3   parent    68.99  74.04648 -5.056e+00       1 3.402
-#&gt;     7   parent    52.32  49.82092  2.499e+00       1 3.402
-#&gt;     7   parent    55.13  49.82092  5.309e+00       1 3.402
-#&gt;    14   parent    27.27  24.90288  2.367e+00       1 3.402
-#&gt;    14   parent    26.64  24.90288  1.737e+00       1 3.402
-#&gt;    21   parent    11.50  12.44765 -9.476e-01       1 3.402
-#&gt;    21   parent    11.64  12.44765 -8.076e-01       1 3.402
-#&gt;    35   parent     2.85   3.11002 -2.600e-01       1 3.402
-#&gt;    35   parent     2.91   3.11002 -2.000e-01       1 3.402
-#&gt;    50   parent     0.69   0.70375 -1.375e-02       1 3.402
-#&gt;    50   parent     0.63   0.70375 -7.375e-02       1 3.402
-#&gt;    75   parent     0.05   0.05913 -9.134e-03       1 3.402
-#&gt;    75   parent     0.06   0.05913  8.662e-04       1 3.402
-#&gt;     0       m1     0.00   0.00000  0.000e+00       2 2.722
-#&gt;     0       m1     0.00   0.00000  0.000e+00       2 2.722
-#&gt;     1       m1     4.84   4.81328  2.672e-02       2 2.722
-#&gt;     1       m1     5.64   4.81328  8.267e-01       2 2.722
-#&gt;     3       m1    12.91  13.04779 -1.378e-01       2 2.722
-#&gt;     3       m1    12.96  13.04779 -8.779e-02       2 2.722
-#&gt;     7       m1    22.97  25.07615 -2.106e+00       2 2.722
-#&gt;     7       m1    24.47  25.07615 -6.062e-01       2 2.722
-#&gt;    14       m1    41.69  36.70729  4.983e+00       2 2.722
-#&gt;    14       m1    33.21  36.70729 -3.497e+00       2 2.722
-#&gt;    21       m1    44.37  41.65050  2.720e+00       2 2.722
-#&gt;    21       m1    46.44  41.65050  4.790e+00       2 2.722
-#&gt;    35       m1    41.22  43.28866 -2.069e+00       2 2.722
-#&gt;    35       m1    37.95  43.28866 -5.339e+00       2 2.722
-#&gt;    50       m1    41.19  41.19339 -3.386e-03       2 2.722
-#&gt;    50       m1    40.01  41.19339 -1.183e+00       2 2.722
-#&gt;    75       m1    40.09  36.43820  3.652e+00       2 2.722
-#&gt;    75       m1    33.85  36.43820 -2.588e+00       2 2.722
-#&gt;   100       m1    31.04  31.98971 -9.497e-01       2 2.722
-#&gt;   100       m1    33.13  31.98971  1.140e+00       2 2.722
-#&gt;   120       m1    25.15  28.80897 -3.659e+00       2 2.722
-#&gt;   120       m1    33.31  28.80897  4.501e+00       2 2.722</div><div class='input'>
-</div></pre>
+<span class='fu'>summary</span>(<span class='no'>f.w.man.irls</span>)
+<span class='co'># }</span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -1346,7 +591,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html
index 5703b188..c1287905 100644
--- a/docs/reference/mkinmod.html
+++ b/docs/reference/mkinmod.html
@@ -18,12 +18,24 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Function to set up a kinetic model with one or more state variables — mkinmod" />
 
+<meta property="og:description" content="The function usually takes several expressions, each assigning a compound name to 
+  a list, specifying the kinetic model type and reaction or transfer to other
+  observed compartments. Instead of specifying several expressions, a list
+  of lists can be given in the speclist argument.
+For the definition of model types and their parameters, the equations given
+  in the FOCUS and NAFTA guidance documents are used." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +82,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +98,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -182,7 +192,7 @@
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
 <p>NAFTA Technical Working Group on Pesticides (not dated) Guidance for
   Evaluating and Calculating Degradation Kinetics in Environmental 
   Media</p>
@@ -196,36 +206,17 @@
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
-
+</div><span class='co'># NOT RUN {</span>
 <span class='co'># The above model used to be specified like this, before the advent of mkinsub()</span>
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>),
-  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))
+
 <span class='co'># Show details of creating the C function</span>
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
-  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; Compilation argument:
-#&gt;  /usr/lib/R/bin/R CMD SHLIB file519167a14b3a.c 2&gt; file519167a14b3a.c.err.txt 
-#&gt; Program source:
-#&gt;   1: #include &lt;R.h&gt;
-#&gt;   2: 
-#&gt;   3: 
-#&gt;   4: static double parms [3];
-#&gt;   5: #define k_parent_sink parms[0]
-#&gt;   6: #define k_parent_m1 parms[1]
-#&gt;   7: #define k_m1_sink parms[2]
-#&gt;   8: 
-#&gt;   9: void initpar(void (* odeparms)(int *, double *)) {
-#&gt;  10:     int N = 3;
-#&gt;  11:     odeparms(&amp;N, parms);
-#&gt;  12: }
-#&gt;  13: 
-#&gt;  14: 
-#&gt;  15: void func ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) {
-#&gt;  16: 
-#&gt;  17: f[0] = - k_parent_sink * y[0] - k_parent_m1 * y[0];
-#&gt;  18: f[1] = + k_parent_m1 * y[0] - k_m1_sink * y[1];
-#&gt;  19: }</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+
 <span class='co'># If we have several parallel metabolites </span>
 <span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span>
 <span class='no'>m_synth_DFOP_par</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"DFOP"</span>, <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>)),
@@ -235,7 +226,8 @@
 
 <span class='no'>fit_DFOP_par_c</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_DFOP_par</span>,
                           <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>12</span>]]$<span class='no'>data</span>,
-                          <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div></pre>
+                          <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='co'># }</span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -264,7 +256,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinparplot-1.png b/docs/reference/mkinparplot-1.png
new file mode 100644
index 00000000..42811535
--- /dev/null
+++ b/docs/reference/mkinparplot-1.png
diff --git a/docs/reference/mkinparplot-4.png b/docs/reference/mkinparplot-4.png
deleted file mode 100644
index c9f4aadb..00000000
--- a/docs/reference/mkinparplot-4.png
+++ /dev/null
diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html
index 67ba6052..30954168 100644
--- a/docs/reference/mkinparplot.html
+++ b/docs/reference/mkinparplot.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Function to plot the confidence intervals obtained using mkinfit — mkinparplot" />
 
+<meta property="og:description" content="This function plots the confidence intervals for the parameters
+  fitted using mkinfit." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +94,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -132,7 +135,7 @@
   <span class='kw'>T245</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"phenol"</span>), <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
   <span class='kw'>phenol</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"anisole"</span>)),
   <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='fu'>subset</span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>mkinparplot</span>(<span class='no'>fit</span>)</div><img src='mkinparplot-4.png' alt='' width='540' height='400' /></pre>
+<span class='fu'>mkinparplot</span>(<span class='no'>fit</span>)</div><div class='img'><img src='mkinparplot-1.png' alt='' width='700' height='432.632880098887' /></div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -157,7 +160,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinplot.html b/docs/reference/mkinplot.html
index 7c875ebe..5df3f872 100644
--- a/docs/reference/mkinplot.html
+++ b/docs/reference/mkinplot.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — mkinplot" />
 
+<meta property="og:description" content="Deprecated function. It now only calls the plot method plot.mkinfit." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -152,7 +154,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html
index b3453e6c..df6316e1 100644
--- a/docs/reference/mkinpredict.html
+++ b/docs/reference/mkinpredict.html
@@ -18,12 +18,21 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Produce predictions from a kinetic model using specific parameters — mkinpredict" />
 
+<meta property="og:description" content="This function produces a time series for all the observed variables in a
+  kinetic model as specified by mkinmod, using a specific set of
+  kinetic parameters and initial values for the state variables." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +79,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +95,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -299,17 +306,17 @@
                 <span class='fu'>c</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'>seq</span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
                 <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#&gt;     time   parent       m1
 #&gt; 201   20 4.978707 27.46227</div><div class='output co'>#&gt;    user  system elapsed 
-#&gt;   0.004   0.000   0.003 </div><div class='input'>  <span class='fu'>system.time</span>(
+#&gt;   0.003   0.000   0.003 </div><div class='input'>  <span class='fu'>system.time</span>(
     <span class='fu'>print</span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'>c</span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
                 <span class='fu'>c</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'>seq</span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
                 <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#&gt;     time   parent       m1
 #&gt; 201   20 4.978707 27.46227</div><div class='output co'>#&gt;    user  system elapsed 
-#&gt;   0.000   0.000   0.001 </div><div class='input'>  <span class='fu'>system.time</span>(
+#&gt;   0.002   0.000   0.001 </div><div class='input'>  <span class='fu'>system.time</span>(
     <span class='fu'>print</span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'>c</span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
                 <span class='fu'>c</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'>seq</span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
                 <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#&gt;     time   parent       m1
 #&gt; 201   20 4.978707 27.46227</div><div class='output co'>#&gt;    user  system elapsed 
-#&gt;   0.032   0.000   0.032 </div></pre>
+#&gt;   0.031   0.000   0.031 </div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -334,7 +341,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinresplot-1.png b/docs/reference/mkinresplot-1.png
new file mode 100644
index 00000000..8636baf2
--- /dev/null
+++ b/docs/reference/mkinresplot-1.png
diff --git a/docs/reference/mkinresplot-4.png b/docs/reference/mkinresplot-4.png
deleted file mode 100644
index 5f3a65e3..00000000
--- a/docs/reference/mkinresplot-4.png
+++ /dev/null
diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html
index 5cb9fa96..036917f1 100644
--- a/docs/reference/mkinresplot.html
+++ b/docs/reference/mkinresplot.html
@@ -18,12 +18,22 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Function to plot residuals stored in an mkin object — mkinresplot" />
 
+<meta property="og:description" content="This function plots the residuals for the specified subset of the 
+  observed variables from an mkinfit object. A combined plot of the fitted
+  model and the residuals can be obtained using plot.mkinfit
+  using the argument show_residuals = TRUE." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +96,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -175,7 +180,7 @@
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'><span class='no'>model</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>mkinresplot</span>(<span class='no'>fit</span>, <span class='st'>"m1"</span>)</div><img src='mkinresplot-4.png' alt='' width='540' height='400' /></pre>
+<span class='fu'>mkinresplot</span>(<span class='no'>fit</span>, <span class='st'>"m1"</span>)</div><div class='img'><img src='mkinresplot-1.png' alt='' width='700' height='432.632880098887' /></div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -202,7 +207,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mkinsub.html b/docs/reference/mkinsub.html
index 257d3f89..7724220f 100644
--- a/docs/reference/mkinsub.html
+++ b/docs/reference/mkinsub.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Function to set up a kinetic submodel for one state variable — mkinsub" />
 
+<meta property="og:description" content="This is a convenience function to set up the lists used as arguments for
+  mkinmod." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +94,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -182,7 +185,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/mmkin-12.png b/docs/reference/mmkin-12.png
deleted file mode 100644
index 9e40d451..00000000
--- a/docs/reference/mmkin-12.png
+++ /dev/null
diff --git a/docs/reference/mmkin-14.png b/docs/reference/mmkin-14.png
deleted file mode 100644
index 72cfc5e7..00000000
--- a/docs/reference/mmkin-14.png
+++ /dev/null
diff --git a/docs/reference/mmkin-15.png b/docs/reference/mmkin-15.png
deleted file mode 100644
index e8a23a55..00000000
--- a/docs/reference/mmkin-15.png
+++ /dev/null
diff --git a/docs/reference/mmkin-16.png b/docs/reference/mmkin-16.png
deleted file mode 100644
index 0b315b1a..00000000
--- a/docs/reference/mmkin-16.png
+++ /dev/null
diff --git a/docs/reference/mmkin-17.png b/docs/reference/mmkin-17.png
deleted file mode 100644
index 01bb3ae3..00000000
--- a/docs/reference/mmkin-17.png
+++ /dev/null
diff --git a/docs/reference/mmkin-18.png b/docs/reference/mmkin-18.png
deleted file mode 100644
index b98940ff..00000000
--- a/docs/reference/mmkin-18.png
+++ /dev/null
diff --git a/docs/reference/mmkin-19.png b/docs/reference/mmkin-19.png
deleted file mode 100644
index b5ac70f7..00000000
--- a/docs/reference/mmkin-19.png
+++ /dev/null
diff --git a/docs/reference/mmkin-20.png b/docs/reference/mmkin-20.png
deleted file mode 100644
index c2e9e5ae..00000000
--- a/docs/reference/mmkin-20.png
+++ /dev/null
diff --git a/docs/reference/mmkin-21.png b/docs/reference/mmkin-21.png
deleted file mode 100644
index 7e15e1b3..00000000
--- a/docs/reference/mmkin-21.png
+++ /dev/null
diff --git a/docs/reference/mmkin-23.png b/docs/reference/mmkin-23.png
deleted file mode 100644
index 45e8efc1..00000000
--- a/docs/reference/mmkin-23.png
+++ /dev/null
diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html
index d649563f..e97e3f81 100644
--- a/docs/reference/mmkin.html
+++ b/docs/reference/mmkin.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin" />
 
+<meta property="og:description" content="This function calls mkinfit on all combinations of models and datasets
+  specified in its first two arguments." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +78,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +94,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -152,45 +158,39 @@
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>
+    <pre class="examples"><span class='co'># NOT RUN {</span>
 <span class='no'>m_synth_SFO_lin</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M1"</span>),
                            <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M2"</span>),
-                           <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+                           <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)
+
 <span class='no'>m_synth_FOMC_lin</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"FOMC"</span>, <span class='st'>"M1"</span>),
                             <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M2"</span>),
-                            <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+                            <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)
+
 <span class='no'>models</span> <span class='kw'>&lt;-</span> <span class='fu'>list</span>(<span class='kw'>SFO_lin</span> <span class='kw'>=</span> <span class='no'>m_synth_SFO_lin</span>, <span class='kw'>FOMC_lin</span> <span class='kw'>=</span> <span class='no'>m_synth_FOMC_lin</span>)
 <span class='no'>datasets</span> <span class='kw'>&lt;-</span> <span class='fu'>lapply</span>(<span class='no'>synthetic_data_for_UBA_2014</span>[<span class='fl'>1</span>:<span class='fl'>3</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='no'>x</span>$<span class='no'>data</span>)
 <span class='fu'>names</span>(<span class='no'>datasets</span>) <span class='kw'>&lt;-</span> <span class='fu'>paste</span>(<span class='st'>"Dataset"</span>, <span class='fl'>1</span>:<span class='fl'>3</span>)
 
 <span class='no'>time_default</span> <span class='kw'>&lt;-</span> <span class='fu'>system.time</span>(<span class='no'>fits.0</span> <span class='kw'>&lt;-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))
-<span class='no'>time_1</span> <span class='kw'>&lt;-</span> <span class='fu'>system.time</span>(<span class='no'>fits.4</span> <span class='kw'>&lt;-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#&gt; <span class='warning'>Warning: Optimisation by method Port did not converge.</span>
-#&gt; <span class='warning'>Convergence code is 1</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Optimisation by method Port did not converge.</span>
-#&gt; <span class='warning'>Convergence code is 1</span></div><div class='input'>
-<span class='no'>time_default</span></div><div class='output co'>#&gt;    user  system elapsed 
-#&gt;  15.992   0.188  11.440 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#&gt;    user  system elapsed 
-#&gt;  24.576   0.000  24.578 </div><div class='input'>
-<span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span>]])</div><div class='output co'>#&gt; $ff
-#&gt;   parent_M1 parent_sink       M1_M2     M1_sink 
-#&gt;   0.7340479   0.2659521   0.7505687   0.2494313 
-#&gt; 
-#&gt; $SFORB
-#&gt; logical(0)
-#&gt; 
-#&gt; $distimes
-#&gt;              DT50       DT90
-#&gt; parent  0.8777689   2.915885
-#&gt; M1      2.3257456   7.725960
-#&gt; M2     33.7200862 112.015702
-#&gt; </div><div class='input'>
+<span class='no'>time_1</span> <span class='kw'>&lt;-</span> <span class='fu'>system.time</span>(<span class='no'>fits.4</span> <span class='kw'>&lt;-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))
+
+<span class='no'>time_default</span>
+<span class='no'>time_1</span>
+
+<span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span>]])
+
 <span class='co'># plot.mkinfit handles rows or columns of mmkin result objects</span>
-<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ])</div><img src='mmkin-15.png' alt='' width='540' height='400' /><div class='input'><span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ], <span class='kw'>obs_var</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>))</div><img src='mmkin-17.png' alt='' width='540' height='400' /><div class='input'><span class='fu'>plot</span>(<span class='no'>fits.0</span>[, <span class='fl'>1</span>])</div><img src='mmkin-19.png' alt='' width='540' height='400' /><div class='input'><span class='co'># Use double brackets to extract a single mkinfit object, which will be plotted</span>
+<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ])
+<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ], <span class='kw'>obs_var</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>))
+<span class='fu'>plot</span>(<span class='no'>fits.0</span>[, <span class='fl'>1</span>])
+<span class='co'># Use double brackets to extract a single mkinfit object, which will be plotted</span>
 <span class='co'># by plot.mkinfit and can be plotted using plot_sep</span>
-<span class='fu'>plot</span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]], <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><img src='mmkin-21.png' alt='' width='540' height='400' /><div class='input'><span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]])
+<span class='fu'>plot</span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]], <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]])
 <span class='co'># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span>
 <span class='co'># allow to plot the observed variables separately</span>
-<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, <span class='fl'>1</span>])</div><img src='mmkin-23.png' alt='' width='540' height='400' /><div class='input'>
-</div></pre>
+<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, <span class='fl'>1</span>])
+<span class='co'># }</span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -217,7 +217,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/plot.mkinfit-1.png b/docs/reference/plot.mkinfit-1.png
new file mode 100644
index 00000000..2bb8f5dd
--- /dev/null
+++ b/docs/reference/plot.mkinfit-1.png
diff --git a/docs/reference/plot.mkinfit-10.png b/docs/reference/plot.mkinfit-10.png
deleted file mode 100644
index 48ab5271..00000000
--- a/docs/reference/plot.mkinfit-10.png
+++ /dev/null
diff --git a/docs/reference/plot.mkinfit-2.png b/docs/reference/plot.mkinfit-2.png
new file mode 100644
index 00000000..22a3f8b0
--- /dev/null
+++ b/docs/reference/plot.mkinfit-2.png
diff --git a/docs/reference/plot.mkinfit-3.png b/docs/reference/plot.mkinfit-3.png
new file mode 100644
index 00000000..93e859c7
--- /dev/null
+++ b/docs/reference/plot.mkinfit-3.png
diff --git a/docs/reference/plot.mkinfit-4.png b/docs/reference/plot.mkinfit-4.png
index cb52d4ac..27edd6f3 100644
--- a/docs/reference/plot.mkinfit-4.png
+++ b/docs/reference/plot.mkinfit-4.png
diff --git a/docs/reference/plot.mkinfit-6.png b/docs/reference/plot.mkinfit-6.png
deleted file mode 100644
index 8e0faa21..00000000
--- a/docs/reference/plot.mkinfit-6.png
+++ /dev/null
diff --git a/docs/reference/plot.mkinfit-8.png b/docs/reference/plot.mkinfit-8.png
deleted file mode 100644
index 129f1445..00000000
--- a/docs/reference/plot.mkinfit-8.png
+++ /dev/null
diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html
index 0af2bbf6..4bdad93f 100644
--- a/docs/reference/plot.mkinfit.html
+++ b/docs/reference/plot.mkinfit.html
@@ -18,12 +18,24 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit" />
 
+<meta property="og:description" content="Solves the differential equations with the optimised and fixed parameters
+  from a previous successful call to mkinfit and plots
+  the observed data together with the solution of the fitted model.
+If the current plot device is a tikz device,
+  then latex is being used for the formatting of the chi2 error level, 
+  if show_errmin = TRUE." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +98,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -237,12 +244,12 @@ plot_sep(fit, sep_obs = TRUE,  show_residuals = TRUE, show_errmin = TRUE, &#8230
 <span class='co'># parent to sink included, use Levenberg-Marquardt for speed</span>
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Parent"</span>),
                    <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span> ))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)
-<span class='fu'>plot</span>(<span class='no'>fit</span>)</div><img src='plot.mkinfit-4.png' alt='' width='540' height='400' /><div class='input'><span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><img src='plot.mkinfit-6.png' alt='' width='540' height='400' /><div class='input'>
+<span class='fu'>plot</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'><span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-2.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>
 <span class='co'># Show the observed variables separately</span>
-<span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><img src='plot.mkinfit-8.png' alt='' width='540' height='400' /><div class='input'>
+<span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><div class='img'><img src='plot.mkinfit-3.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>
 <span class='co'># Show the observed variables separately, with residuals</span>
 <span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>),
-     <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><img src='plot.mkinfit-10.png' alt='' width='540' height='400' /><div class='input'>
+     <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-4.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>
 <span class='co'># The same can be obtained with less typing, using the convenience function plot_sep</span>
 <span class='fu'>plot_sep</span>(<span class='no'>fit</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div></pre>
   </div>
@@ -269,7 +276,7 @@ plot_sep(fit, sep_obs = TRUE,  show_residuals = TRUE, show_errmin = TRUE, &#8230
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/plot.mmkin-1.png b/docs/reference/plot.mmkin-1.png
new file mode 100644
index 00000000..2554b68b
--- /dev/null
+++ b/docs/reference/plot.mmkin-1.png
diff --git a/docs/reference/plot.mmkin-2.png b/docs/reference/plot.mmkin-2.png
index 21af1e7b..9a66294f 100644
--- a/docs/reference/plot.mmkin-2.png
+++ b/docs/reference/plot.mmkin-2.png
diff --git a/docs/reference/plot.mmkin-3.png b/docs/reference/plot.mmkin-3.png
new file mode 100644
index 00000000..b0f7fa21
--- /dev/null
+++ b/docs/reference/plot.mmkin-3.png
diff --git a/docs/reference/plot.mmkin-4.png b/docs/reference/plot.mmkin-4.png
deleted file mode 100644
index 3004f48f..00000000
--- a/docs/reference/plot.mmkin-4.png
+++ /dev/null
diff --git a/docs/reference/plot.mmkin-6.png b/docs/reference/plot.mmkin-6.png
deleted file mode 100644
index 02ed2ab1..00000000
--- a/docs/reference/plot.mmkin-6.png
+++ /dev/null
diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html
index aa3df77a..11da6685 100644
--- a/docs/reference/plot.mmkin.html
+++ b/docs/reference/plot.mmkin.html
@@ -18,12 +18,23 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin" />
 
+<meta property="og:description" content="When x is a row selected from an mmkin object ([.mmkin), the same model
+  fitted for at least one dataset is shown. When it is a column, the fit of at least one model
+  to the same dataset is shown.
+If the current plot device is a tikz device,
+  then latex is being used for the formatting of the chi2 error level." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +97,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -165,11 +171,11 @@
   <span class='no'>fits</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'>c</span>(<span class='st'>"FOMC"</span>, <span class='st'>"HS"</span>),
                 <span class='fu'>list</span>(<span class='st'>"FOCUS B"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_B</span>, <span class='st'>"FOCUS C"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_C</span>), <span class='co'># named list for titles</span>
                 <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>)
-  <span class='fu'>plot</span>(<span class='no'>fits</span>[, <span class='st'>"FOCUS C"</span>])</div><img src='plot.mmkin-2.png' alt='' width='540' height='400' /><div class='input'>  <span class='fu'>plot</span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ])</div><img src='plot.mmkin-4.png' alt='' width='540' height='400' /><div class='input'>
+  <span class='fu'>plot</span>(<span class='no'>fits</span>[, <span class='st'>"FOCUS C"</span>])</div><div class='img'><img src='plot.mmkin-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>  <span class='fu'>plot</span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ])</div><div class='img'><img src='plot.mmkin-2.png' alt='' width='700' height='432.632880098887' /></div><div class='input'>
   <span class='co'># We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</span>
   <span class='co'># height should be smaller than the plot width (this is not possible for the html pages</span>
   <span class='co'># generated by pkgdown, as far as I know).</span>
-  <span class='fu'>plot</span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, <span class='st'>"FOCUS C"</span>]) <span class='co'># same as plot(fits[1, 2])</span></div><img src='plot.mmkin-6.png' alt='' width='540' height='400' /></pre>
+  <span class='fu'>plot</span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, <span class='st'>"FOCUS C"</span>]) <span class='co'># same as plot(fits[1, 2])</span></div><div class='img'><img src='plot.mmkin-3.png' alt='' width='700' height='432.632880098887' /></div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -194,7 +200,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/print.mkinds.html b/docs/reference/print.mkinds.html
index 14342f21..8e0d18b2 100644
--- a/docs/reference/print.mkinds.html
+++ b/docs/reference/print.mkinds.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Print mkinds objects — print.mkinds" />
 
+<meta property="og:description" content="Print mkinds objects." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -143,7 +145,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/print.mkinmod.html b/docs/reference/print.mkinmod.html
index 33782b7d..db15cc60 100644
--- a/docs/reference/print.mkinmod.html
+++ b/docs/reference/print.mkinmod.html
@@ -18,12 +18,19 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Print mkinmod objects — print.mkinmod" />
 
+<meta property="og:description" content="Print mkinmod objects in a way that the user finds his way to get to its components." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +93,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -164,7 +166,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/schaefer07_complex_case-4.png b/docs/reference/schaefer07_complex_case-4.png
deleted file mode 100644
index b90185a1..00000000
--- a/docs/reference/schaefer07_complex_case-4.png
+++ /dev/null
diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html
index 605f572e..6c53f805 100644
--- a/docs/reference/schaefer07_complex_case.html
+++ b/docs/reference/schaefer07_complex_case.html
@@ -18,12 +18,21 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case" />
 
+<meta property="og:description" content="This dataset was used for a comparison of KinGUI and ModelMaker to check the
+  software quality of KinGUI in the original publication (Schäfer et al., 2007).
+  The results from the fitting are also included." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -86,12 +95,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -140,24 +144,12 @@
   <span class='kw'>A1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"A2"</span>),
   <span class='kw'>B1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
   <span class='kw'>C1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
-  <span class='kw'>A2</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+  <span class='kw'>A2</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>  </div><span class='co'># NOT RUN {</span>
     <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-    <span class='fu'>plot</span>(<span class='no'>fit</span>)</div><img src='schaefer07_complex_case-4.png' alt='' width='540' height='400' /><div class='input'>    <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; $ff
-#&gt;   parent_A1   parent_B1   parent_C1 parent_sink       A1_A2     A1_sink 
-#&gt;   0.3809618   0.1954667   0.4235715   0.0000000   0.4479596   0.5520404 
-#&gt; 
-#&gt; $SFORB
-#&gt; logical(0)
-#&gt; 
-#&gt; $distimes
-#&gt;            DT50      DT90
-#&gt; parent 13.95078  46.34349
-#&gt; A1     49.75345 165.27739
-#&gt; B1     37.26908 123.80521
-#&gt; C1     11.23130  37.30957
-#&gt; A2     28.50652  94.69662
-#&gt; </div><div class='input'>
- <span class='co'># Compare with the results obtained in the original publication</span>
+    <span class='fu'>plot</span>(<span class='no'>fit</span>)
+    <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)
+
+<span class='co'># }</span><div class='input'> <span class='co'># Compare with the results obtained in the original publication</span>
  <span class='fu'>print</span>(<span class='no'>schaefer07_complex_results</span>)</div><div class='output co'>#&gt;         compound          parameter  KinGUI ModelMaker deviation
 #&gt; 1         parent   degradation rate  0.0496     0.0506       2.0
 #&gt; 2         parent               DT50 13.9900    13.6900       2.2
@@ -194,7 +186,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/sigma_rl.html b/docs/reference/sigma_rl.html
index b7c93961..868c0d4f 100644
--- a/docs/reference/sigma_rl.html
+++ b/docs/reference/sigma_rl.html
@@ -18,12 +18,21 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
-
+  
+  
+<meta property="og:title" content="Two component error model of Rocke and Lorenzato — sigma_rl" />
+
+<meta property="og:description" content="Function describing the standard deviation of the measurement error 
+  in dependence of the measured value \(y\):
+$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$" />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -157,7 +166,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html
index f20e3948..977ff8d8 100644
--- a/docs/reference/summary.mkinfit.html
+++ b/docs/reference/summary.mkinfit.html
@@ -18,12 +18,22 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Summary method for class "mkinfit" — summary.mkinfit" />
 
+<meta property="og:description" content="Lists model equations, the summary as returned by summary.modFit,
+  the chi2 error levels calculated according to FOCUS guidance (2006) as far
+  as defined therein, and optionally the data, consisting of observed, predicted
+  and residual values." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +80,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +96,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -177,21 +185,21 @@
   Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
   Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
+  <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>  <span class='fu'>summary</span>(<span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>)), <span class='no'>FOCUS_2006_A</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#&gt; mkin version:    0.9.46 
-#&gt; R version:       3.4.1 
-#&gt; Date of fit:     Sat Jul 29 15:15:30 2017 
-#&gt; Date of summary: Sat Jul 29 15:15:30 2017 
+    <pre class="examples"><div class='input'>  <span class='fu'>summary</span>(<span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>)), <span class='no'>FOCUS_2006_A</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.47.1 
+#&gt; R version used for fitting:       3.4.3 
+#&gt; Date of fit:     Thu Mar  1 14:26:27 2018 
+#&gt; Date of summary: Thu Mar  1 14:26:27 2018 
 #&gt; 
 #&gt; Equations:
 #&gt; d_parent/dt = - k_parent_sink * parent
 #&gt; 
 #&gt; Model predictions using solution type analytical 
 #&gt; 
-#&gt; Fitted with method Port using 35 model solutions performed in 0.084 s
+#&gt; Fitted with method Port using 35 model solutions performed in 0.076 s
 #&gt; 
 #&gt; Weighting: none
 #&gt; 
@@ -277,7 +285,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/synthetic_data_for_UBA.html b/docs/reference/synthetic_data_for_UBA.html
index f9603f34..192e8dc2 100644
--- a/docs/reference/synthetic_data_for_UBA.html
+++ b/docs/reference/synthetic_data_for_UBA.html
@@ -18,12 +18,31 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014" />
 
+<meta property="og:description" content="The 12 datasets were generated using four different models and three different
+ variance components. The four models are either the SFO or the DFOP model with either
+ two sequential or two parallel metabolites.
+Variance component 'a' is based on a normal distribution with standard deviation of 3,
+ Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
+ Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
+ minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
+ for the increase of the standard deviation with y.
+Initial concentrations for metabolites and all values where adding the variance component resulted
+ in a value below the assumed limit of detection of 0.1 were set to NA.
+As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
+ with two sequential metabolites (linear pathway), with added variance component 'a'.
+Compare also the code in the example section to see the degradation models." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -136,7 +155,7 @@
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>
+    <pre class="examples"># NOT RUN {
 # The data have been generated using the following kinetic models
 m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"),
                            M1 = list(type = "SFO", to = "M2"),
@@ -235,11 +254,8 @@ fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data,
                quiet = TRUE)
 plot_sep(fit)
 summary(fit)
-
-</div><div class='output co'>#&gt; <span class='error'>Error: &lt;text&gt;:68:43: Unerwartete(s) SPECIAL</span>
-#&gt; <span class='error'>67: </span>
-#&gt; <span class='error'>68:   d_rep[d_rep$time == 0 &amp; d_rep$name &lt;!-- %in%</span>
-#&gt; <span class='error'>                                              ^</span></div></pre>
+# }
+</pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -261,7 +277,7 @@ summary(fit)
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/synthetic_data_for_UBA_2014-10.png b/docs/reference/synthetic_data_for_UBA_2014-10.png
deleted file mode 100644
index 7e15e1b3..00000000
--- a/docs/reference/synthetic_data_for_UBA_2014-10.png
+++ /dev/null
diff --git a/docs/reference/test_data_from_UBA_2014-12.png b/docs/reference/test_data_from_UBA_2014-12.png
deleted file mode 100644
index 6738f3a0..00000000
--- a/docs/reference/test_data_from_UBA_2014-12.png
+++ /dev/null
diff --git a/docs/reference/test_data_from_UBA_2014-16.png b/docs/reference/test_data_from_UBA_2014-16.png
deleted file mode 100644
index 6738f3a0..00000000
--- a/docs/reference/test_data_from_UBA_2014-16.png
+++ /dev/null
diff --git a/docs/reference/test_data_from_UBA_2014-4.png b/docs/reference/test_data_from_UBA_2014-4.png
deleted file mode 100644
index 8c65e604..00000000
--- a/docs/reference/test_data_from_UBA_2014-4.png
+++ /dev/null
diff --git a/docs/reference/test_data_from_UBA_2014-6.png b/docs/reference/test_data_from_UBA_2014-6.png
deleted file mode 100644
index 8c65e604..00000000
--- a/docs/reference/test_data_from_UBA_2014-6.png
+++ /dev/null
diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html
index ed2ccd9c..c4292d9c 100644
--- a/docs/reference/test_data_from_UBA_2014.html
+++ b/docs/reference/test_data_from_UBA_2014.html
@@ -18,12 +18,20 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014" />
 
+<meta property="og:description" content="The datasets were used for the comparative validation of several kinetic evaluation
+ software packages (Ranke, 2014)." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -123,7 +131,7 @@
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>
+    <pre class="examples"><div class='input'>  </div><span class='co'># NOT RUN {</span>
   <span class='co'># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span>
   <span class='co'># guidance. Due to the strong correlation of the parameter estimates, the</span>
   <span class='co'># covariance matrix is not returned. Note that level P-II evaluations are</span>
@@ -131,57 +139,27 @@
   <span class='co'># large parameter correlations, among other reasons (e.g. the adequacy of the</span>
   <span class='co'># model).</span>
   <span class='no'>m_ws</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent_w</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_s"</span>),
-                  <span class='kw'>parent_s</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_w"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>  <span class='no'>f_river</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_ws</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-  <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_river</span>)</div><img src='test_data_from_UBA_2014-4.png' alt='' width='540' height='400' /><div class='input'>
-  <span class='fu'>summary</span>(<span class='no'>f_river</span>)$<span class='no'>bpar</span></div><div class='output co'>#&gt;                         Estimate se_notrans      t value       Pr(&gt;t) Lower
-#&gt; parent_w_0          9.598567e+01 2.33959810 4.102657e+01 9.568973e-19    NA
-#&gt; k_parent_w_sink     3.603743e-01 0.03497716 1.030313e+01 4.988281e-09    NA
-#&gt; k_parent_w_parent_s 6.031370e-02 0.01746026 3.454342e+00 1.514738e-03    NA
-#&gt; k_parent_s_sink     5.099834e-11 0.10381939 4.912217e-10 5.000000e-01    NA
-#&gt; k_parent_s_parent_w 7.419672e-02 0.11338174 6.543974e-01 2.608057e-01    NA
-#&gt;                     Upper
-#&gt; parent_w_0             NA
-#&gt; k_parent_w_sink        NA
-#&gt; k_parent_w_parent_s    NA
-#&gt; k_parent_s_sink        NA
-#&gt; k_parent_s_parent_w    NA</div><div class='input'>  <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_river</span>)</div><div class='output co'>#&gt;             err.min n.optim df
-#&gt; All data 0.09246946       5  6
-#&gt; parent_w 0.06377096       3  3
-#&gt; parent_s 0.20882324       2  3</div><div class='input'>
+                  <span class='kw'>parent_s</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_w"</span>))
+  <span class='no'>f_river</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_ws</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+  <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_river</span>)
+
+  <span class='fu'>summary</span>(<span class='no'>f_river</span>)$<span class='no'>bpar</span>
+  <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_river</span>)
+
   <span class='co'># This is the evaluation used for the validation of software packages</span>
   <span class='co'># in the expertise from 2014</span>
   <span class='no'>m_soil</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>)),
                     <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M3"</span>),
                     <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M3"</span>),
                     <span class='kw'>M3</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>),
-                    <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+                    <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)
+
   <span class='no'>f_soil</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_soil</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>3</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-  <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_soil</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><img src='test_data_from_UBA_2014-12.png' alt='' width='540' height='400' /><div class='input'>  <span class='fu'>summary</span>(<span class='no'>f_soil</span>)$<span class='no'>bpar</span></div><div class='output co'>#&gt;                   Estimate  se_notrans    t value       Pr(&gt;t)        Lower
-#&gt; parent_0       76.55425583 0.943443834 81.1434164 4.422340e-30 74.602593306
-#&gt; k_parent        0.12081956 0.004815515 25.0896457 1.639665e-18  0.111257526
-#&gt; k_M1            0.84258650 0.930121206  0.9058889 1.871937e-01  0.085876305
-#&gt; k_M2            0.04210878 0.013729902  3.0669396 2.729137e-03  0.021450631
-#&gt; k_M3            0.01122919 0.008044866  1.3958205 8.804914e-02  0.002550985
-#&gt; f_parent_to_M1  0.32240199 0.278620411  1.1571370 1.295466e-01           NA
-#&gt; f_parent_to_M2  0.16099854 0.030548889  5.2701930 1.196191e-05           NA
-#&gt; f_M1_to_M3      0.27921500 0.314732717  0.8871496 1.920907e-01  0.015016888
-#&gt; f_M2_to_M3      0.55641332 0.650247079  0.8556952 2.004966e-01  0.005360551
-#&gt;                      Upper
-#&gt; parent_0       78.50591836
-#&gt; k_parent        0.13120340
-#&gt; k_M1            8.26714671
-#&gt; k_M2            0.08266187
-#&gt; k_M3            0.04942980
-#&gt; f_parent_to_M1          NA
-#&gt; f_parent_to_M2          NA
-#&gt; f_M1_to_M3      0.90777217
-#&gt; f_M2_to_M3      0.99658634</div><div class='input'>  <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_soil</span>)</div><div class='output co'>#&gt;             err.min n.optim df
-#&gt; All data 0.09649963       9 20
-#&gt; parent   0.04721283       2  6
-#&gt; M1       0.26551209       2  5
-#&gt; M2       0.20327575       2  5
-#&gt; M3       0.05196549       3  4</div><div class='input'>  
-</div></pre>
+  <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_soil</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))
+  <span class='fu'>summary</span>(<span class='no'>f_soil</span>)$<span class='no'>bpar</span>
+  <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_soil</span>)
+
+<span class='co'># }</span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -203,7 +181,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html
index bbf15a41..630a5103 100644
--- a/docs/reference/transform_odeparms.html
+++ b/docs/reference/transform_odeparms.html
@@ -18,12 +18,27 @@
 <!-- Font Awesome icons -->
 <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous">
 
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
 <script src="../jquery.sticky-kit.min.js"></script>
 <script src="../pkgdown.js"></script>
+  
+  
+<meta property="og:title" content="Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms" />
 
+<meta property="og:description" content="The transformations are intended to map parameters that should only take
+  on restricted values to the full scale of real numbers. For kinetic rate
+  constants and other paramters that can only take on positive values, a
+  simple log transformation is used. For compositional parameters, such as
+  the formations fractions that should always sum up to 1 and can not be
+  negative, the ilr transformation is used.
+The transformation of sets of formation fractions is fragile, as it supposes
+  the same ordering of the components in forward and backward transformation.
+  This is no problem for the internal use in mkinfit." />
+<meta name="twitter:card" content="summary" />
 <!-- mathjax -->
 <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script>
 
@@ -70,6 +85,9 @@
       <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
     </li>
     <li>
+      <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+    </li>
+    <li>
       <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
     </li>
     <li>
@@ -83,12 +101,7 @@
       </ul>
       
       <ul class="nav navbar-nav navbar-right">
-        <li>
-  <a href="http://github.com/jranke/mkin">
-    <span class="fa fa-github fa-lg"></span>
-     
-  </a>
-</li>
+        
       </ul>
     </div><!--/.nav-collapse -->
   </div><!--/.container -->
@@ -170,10 +183,10 @@
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
 <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>fit</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>) <span class='co'># See transformed and backtransformed parameters</span></div><div class='output co'>#&gt; mkin version:    0.9.46 
-#&gt; R version:       3.4.1 
-#&gt; Date of fit:     Sat Jul 29 15:15:35 2017 
-#&gt; Date of summary: Sat Jul 29 15:15:35 2017 
+<span class='fu'>summary</span>(<span class='no'>fit</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>) <span class='co'># See transformed and backtransformed parameters</span></div><div class='output co'>#&gt; mkin version used for fitting:    0.9.47.1 
+#&gt; R version used for fitting:       3.4.3 
+#&gt; Date of fit:     Thu Mar  1 14:26:28 2018 
+#&gt; Date of summary: Thu Mar  1 14:26:28 2018 
 #&gt; 
 #&gt; Equations:
 #&gt; d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
@@ -181,7 +194,7 @@
 #&gt; 
 #&gt; Model predictions using solution type deSolve 
 #&gt; 
-#&gt; Fitted with method Port using 153 model solutions performed in 0.608 s
+#&gt; Fitted with method Port using 153 model solutions performed in 0.571 s
 #&gt; 
 #&gt; Weighting: none
 #&gt; 
@@ -245,84 +258,10 @@
 #&gt;           DT50   DT90
 #&gt; parent   7.023  23.33
 #&gt; m1     131.761 437.70</div><div class='input'>
-
+</div><span class='co'># NOT RUN {</span>
 <span class='no'>fit.2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>transform_rates</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version:    0.9.46 
-#&gt; R version:       3.4.1 
-#&gt; Date of fit:     Sat Jul 29 15:15:36 2017 
-#&gt; Date of summary: Sat Jul 29 15:15:36 2017 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
-#&gt; d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 352 model solutions performed in 1.401 s
-#&gt; 
-#&gt; Weighting: none
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                  value   type
-#&gt; parent_0      100.7500  state
-#&gt; k_parent_sink   0.1000 deparm
-#&gt; k_parent_m1     0.1001 deparm
-#&gt; k_m1_sink       0.1002 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                  value lower upper
-#&gt; parent_0      100.7500  -Inf   Inf
-#&gt; k_parent_sink   0.1000     0   Inf
-#&gt; k_parent_m1     0.1001     0   Inf
-#&gt; k_m1_sink       0.1002     0   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                Estimate Std. Error     Lower     Upper
-#&gt; parent_0      99.600000  1.6140000 96.330000 1.029e+02
-#&gt; k_parent_sink  0.047920  0.0037500  0.040310 5.553e-02
-#&gt; k_parent_m1    0.050780  0.0020940  0.046530 5.502e-02
-#&gt; k_m1_sink      0.005261  0.0007159  0.003809 6.713e-03
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;               parent_0 k_parent_sink k_parent_m1 k_m1_sink
-#&gt; parent_0       1.00000        0.6075    -0.06625   -0.1701
-#&gt; k_parent_sink  0.60752        1.0000    -0.08740   -0.6253
-#&gt; k_parent_m1   -0.06625       -0.0874     1.00000    0.4716
-#&gt; k_m1_sink     -0.17006       -0.6253     0.47164    1.0000
-#&gt; 
-#&gt; Residual standard error: 3.211 on 36 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                Estimate t value    Pr(&gt;t)     Lower     Upper
-#&gt; parent_0      99.600000  61.720 2.024e-38 96.330000 1.029e+02
-#&gt; k_parent_sink  0.047920  12.780 3.050e-15  0.040310 5.553e-02
-#&gt; k_parent_m1    0.050780  24.250 3.407e-24  0.046530 5.502e-02
-#&gt; k_m1_sink      0.005261   7.349 5.758e-09  0.003809 6.713e-03
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   6.398       4 15
-#&gt; parent     6.827       3  6
-#&gt; m1         4.490       1  9
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                 ff
-#&gt; parent_sink 0.4855
-#&gt; parent_m1   0.5145
-#&gt; m1_sink     1.0000
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;           DT50   DT90
-#&gt; parent   7.023  23.33
-#&gt; m1     131.761 437.70</div><div class='input'>
-
+<span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>)
+<span class='co'># }</span><div class='input'>
 <span class='no'>initials</span> <span class='kw'>&lt;-</span> <span class='no'>fit</span>$<span class='no'>start</span>$<span class='no'>value</span>
 <span class='fu'>names</span>(<span class='no'>initials</span>) <span class='kw'>&lt;-</span> <span class='fu'>rownames</span>(<span class='no'>fit</span>$<span class='no'>start</span>)
 <span class='no'>transformed</span> <span class='kw'>&lt;-</span> <span class='no'>fit</span>$<span class='no'>start_transformed</span>$<span class='no'>value</span>
@@ -330,162 +269,29 @@
 <span class='fu'>transform_odeparms</span>(<span class='no'>initials</span>, <span class='no'>SFO_SFO</span>)</div><div class='output co'>#&gt;          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
 #&gt;        100.750000         -2.302585         -2.301586         -2.300587 </div><div class='input'><span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO</span>)</div><div class='output co'>#&gt;      parent_0 k_parent_sink   k_parent_m1     k_m1_sink 
 #&gt;      100.7500        0.1000        0.1001        0.1002 </div><div class='input'>
-
+</div><span class='co'># NOT RUN {</span>
 <span class='co'># The case of formation fractions</span>
 <span class='no'>SFO_SFO.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
-  <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+  <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)
+
 <span class='no'>fit.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>fit.ff</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version:    0.9.46 
-#&gt; R version:       3.4.1 
-#&gt; Date of fit:     Sat Jul 29 15:15:37 2017 
-#&gt; Date of summary: Sat Jul 29 15:15:37 2017 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 185 model solutions performed in 0.772 s
-#&gt; 
-#&gt; Weighting: none
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;                   value   type
-#&gt; parent_0       100.7500  state
-#&gt; k_parent         0.1000 deparm
-#&gt; k_m1             0.1001 deparm
-#&gt; f_parent_to_m1   0.5000 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                     value lower upper
-#&gt; parent_0       100.750000  -Inf   Inf
-#&gt; log_k_parent    -2.302585  -Inf   Inf
-#&gt; log_k_m1        -2.301586  -Inf   Inf
-#&gt; f_parent_ilr_1   0.000000  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;                Estimate Std. Error   Lower    Upper
-#&gt; parent_0       99.60000    1.61400 96.3300 102.9000
-#&gt; log_k_parent   -2.31600    0.04187 -2.4010  -2.2310
-#&gt; log_k_m1       -5.24800    0.13610 -5.5230  -4.9720
-#&gt; f_parent_ilr_1  0.04096    0.06477 -0.0904   0.1723
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#&gt; parent_0         1.0000       0.5178  -0.1701        -0.5489
-#&gt; log_k_parent     0.5178       1.0000  -0.3285        -0.5451
-#&gt; log_k_m1        -0.1701      -0.3285   1.0000         0.7466
-#&gt; f_parent_ilr_1  -0.5489      -0.5451   0.7466         1.0000
-#&gt; 
-#&gt; Residual standard error: 3.211 on 36 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;                 Estimate t value    Pr(&gt;t)     Lower     Upper
-#&gt; parent_0       99.600000  61.720 2.024e-38 96.330000 1.029e+02
-#&gt; k_parent        0.098700  23.880 5.701e-24  0.090660 1.074e-01
-#&gt; k_m1            0.005261   7.349 5.758e-09  0.003992 6.933e-03
-#&gt; f_parent_to_m1  0.514500  22.490 4.374e-23  0.468100 5.606e-01
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   6.398       4 15
-#&gt; parent     6.459       2  7
-#&gt; m1         4.690       2  8
-#&gt; 
-#&gt; Resulting formation fractions:
-#&gt;                 ff
-#&gt; parent_m1   0.5145
-#&gt; parent_sink 0.4855
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;           DT50   DT90
-#&gt; parent   7.023  23.33
-#&gt; m1     131.761 437.70</div><div class='input'><span class='no'>initials</span> <span class='kw'>&lt;-</span> <span class='fu'>c</span>(<span class='st'>"f_parent_to_m1"</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)
+<span class='fu'>summary</span>(<span class='no'>fit.ff</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
+<span class='no'>initials</span> <span class='kw'>&lt;-</span> <span class='fu'>c</span>(<span class='st'>"f_parent_to_m1"</span> <span class='kw'>=</span> <span class='fl'>0.5</span>)
 <span class='no'>transformed</span> <span class='kw'>&lt;-</span> <span class='fu'>transform_odeparms</span>(<span class='no'>initials</span>, <span class='no'>SFO_SFO.ff</span>)
-<span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO.ff</span>)</div><div class='output co'>#&gt; f_parent_to_m1 
-#&gt;            0.5 </div><div class='input'>
+<span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO.ff</span>)
+
 <span class='co'># And without sink</span>
 <span class='no'>SFO_SFO.ff.2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
-  <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
+  <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)
+
 
 <span class='no'>fit.ff.2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff.2</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'>summary</span>(<span class='no'>fit.ff.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt; mkin version:    0.9.46 
-#&gt; R version:       3.4.1 
-#&gt; Date of fit:     Sat Jul 29 15:15:38 2017 
-#&gt; Date of summary: Sat Jul 29 15:15:38 2017 
-#&gt; 
-#&gt; Equations:
-#&gt; d_parent/dt = - k_parent * parent
-#&gt; d_m1/dt = + k_parent * parent - k_m1 * m1
-#&gt; 
-#&gt; Model predictions using solution type deSolve 
-#&gt; 
-#&gt; Fitted with method Port using 104 model solutions performed in 0.416 s
-#&gt; 
-#&gt; Weighting: none
-#&gt; 
-#&gt; Starting values for parameters to be optimised:
-#&gt;             value   type
-#&gt; parent_0 100.7500  state
-#&gt; k_parent   0.1000 deparm
-#&gt; k_m1       0.1001 deparm
-#&gt; 
-#&gt; Starting values for the transformed parameters actually optimised:
-#&gt;                   value lower upper
-#&gt; parent_0     100.750000  -Inf   Inf
-#&gt; log_k_parent  -2.302585  -Inf   Inf
-#&gt; log_k_m1      -2.301586  -Inf   Inf
-#&gt; 
-#&gt; Fixed parameter values:
-#&gt;      value  type
-#&gt; m1_0     0 state
-#&gt; 
-#&gt; Optimised, transformed parameters with symmetric confidence intervals:
-#&gt;              Estimate Std. Error Lower  Upper
-#&gt; parent_0       84.790    2.96500 78.78 90.800
-#&gt; log_k_parent   -2.756    0.08088 -2.92 -2.593
-#&gt; log_k_m1       -4.214    0.11150 -4.44 -3.988
-#&gt; 
-#&gt; Parameter correlation:
-#&gt;              parent_0 log_k_parent log_k_m1
-#&gt; parent_0       1.0000      0.11059  0.46156
-#&gt; log_k_parent   0.1106      1.00000  0.06274
-#&gt; log_k_m1       0.4616      0.06274  1.00000
-#&gt; 
-#&gt; Residual standard error: 8.333 on 37 degrees of freedom
-#&gt; 
-#&gt; Backtransformed parameters:
-#&gt; Confidence intervals for internally transformed parameters are asymmetric.
-#&gt; t-test (unrealistically) based on the assumption of normal distribution
-#&gt; for estimators of untransformed parameters.
-#&gt;          Estimate t value    Pr(&gt;t)    Lower    Upper
-#&gt; parent_0 84.79000  28.600 3.938e-27 78.78000 90.80000
-#&gt; k_parent  0.06352  12.360 5.237e-15  0.05392  0.07483
-#&gt; k_m1      0.01478   8.966 4.114e-11  0.01179  0.01853
-#&gt; 
-#&gt; Chi2 error levels in percent:
-#&gt;          err.min n.optim df
-#&gt; All data   19.66       3 16
-#&gt; parent     17.56       2  7
-#&gt; m1         18.71       1  9
-#&gt; 
-#&gt; Estimated disappearance times:
-#&gt;         DT50   DT90
-#&gt; parent 10.91  36.25
-#&gt; m1     46.89 155.75</div><div class='input'>
-</div></pre>
+<span class='fu'>summary</span>(<span class='no'>fit.ff.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
+<span class='co'># }</span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
@@ -510,7 +316,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p>
+  <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p>
 </div>
 
       </footer>
diff --git a/docs/reference/twa.html b/docs/reference/twa.html
deleted file mode 100644
index be76b439..00000000
--- a/docs/reference/twa.html
+++ /dev/null
@@ -1,179 +0,0 @@
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-    <div class="page-header">
-    <h1>Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit</h1>
-    </div>
-
-    
-    <p>This function calculates maximum moving window time weighted average concentrations
-(TWAs) for kinetic models fitted with <code><a href='mkinfit.html'>mkinfit</a></code>. Currently, only
-calculations for the parent are implemented for the SFO, FOMC and DFOP models,
-using the analytical formulas given in the PEC soil section of the FOCUS
-guidance.</p>
-    
-
-    <pre class="usage"><span class='fu'>twa</span>(<span class='no'>fit</span>, <span class='no'>windows</span>)</pre>
-    
-    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a> Arguments</h2>
-    <table class="ref-arguments">
-    <colgroup><col class="name" /><col class="desc" /></colgroup>
-    <tr>
-      <th>fit</th>
-      <td><p>An object of class <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td>
-    </tr>
-    <tr>
-      <th>windows</th>
-      <td><p>The width of the time windows for which the TWAs should be calculated.</p></td>
-    </tr>
-    </table>
-    
-    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
-
-    <p>A numeric vector, named using the <code>windows</code> argument.</p>
-    
-    <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
-
-    <p>FOCUS (2006) &#8220;Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration&#8221; Report of the FOCUS Work Group on Degradation Kinetics,
-  EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p>
-    
-
-    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'>  <span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-  <span class='fu'>twa</span>(<span class='no'>fit</span>, <span class='fu'>c</span>(<span class='fl'>7</span>, <span class='fl'>21</span>))</div><div class='output co'>#&gt;        7       21 
-#&gt; 34.71343 18.22124 </div></pre>
-  </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
-    <h2>Contents</h2>
-    <ul class="nav nav-pills nav-stacked">
-      <li><a href="#arguments">Arguments</a></li>
-      
-      <li><a href="#value">Value</a></li>
-
-      <li><a href="#references">References</a></li>
-      
-      <li><a href="#examples">Examples</a></li>
-    </ul>
-
-    <h2>Author</h2>
-    
-  Johannes Ranke
-
-  </div>
-</div>
-
-      <footer>
-      <div class="copyright">
-  <p>Developed by Johannes Ranke.</p>
-</div>
-
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diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index ccde0c82..9bdfb5c6 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -1,258 +1,248 @@
 <!DOCTYPE html>
-
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 <head>
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-<meta charset="utf-8" />
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+<title>Laboratory Data L1</title>
 
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+};
+</script>
 
-<meta name="author" content="Johannes Ranke" />
+<!-- Styles for R syntax highlighter -->
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+     color: #d14;
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+</style>
 
-<meta name="date" content="2018-01-14" />
+<!-- R syntax highlighter -->
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e0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v%2BPTmTi3E%2FglC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO%2BYOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr%2BH1jR7km9vcvqN9657vffefkv%2BfKxge1X%2B7RdjYUPIESN7gTvRkB%2FRMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F%2BUX4%2BWKS4iBTP0IWxGtZgOdMaV4KTayqHQF%2FVihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g%2FqRCuy3vmQ3W9Vu8qbVbP%2BSzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB%2BLopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9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%2FTAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAA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RTA1Ngd1bmlFMDU3B3VuaUUwNTgHdW5pRTA1OQd1bmlFMDYwB3VuaUUwNjIHdW5pRTA2Mwd1bmlFMDY0B3VuaUUwNjUHdW5pRTA2Ngd1bmlFMDY3B3VuaUUwNjgHdW5pRTA2OQd1bmlFMDcwB3VuaUUwNzEHdW5pRTA3Mgd1bmlFMDczB3VuaUUwNzQHdW5pRTA3NQd1bmlFMDc2B3VuaUUwNzcHdW5pRTA3OAd1bmlFMDc5B3VuaUUwODAHdW5pRTA4MQd1bmlFMDgyB3VuaUUwODMHdW5pRTA4NAd1bmlFMDg1B3VuaUUwODYHdW5pRTA4Nwd1bmlFMDg4B3VuaUUwODkHdW5pRTA5MAd1bmlFMDkxB3VuaUUwOTIHdW5pRTA5Mwd1bmlFMDk0B3VuaUUwOTUHdW5pRTA5Ngd1bmlFMDk3B3VuaUUxMDEHdW5pRTEwMgd1bmlFMTAzB3VuaUUxMDQHdW5pRTEwNQd1bmlFMTA2B3VuaUUxMDcHdW5pRTEwOAd1bmlFMTA5B3VuaUUxMTAHdW5pRTExMQd1bmlFMTEyB3VuaUUxMTMHdW5pRTExNAd1bmlFMTE1B3VuaUUxMTYHdW5pRTExNwd1bmlFMTE4B3VuaUUxMTkHdW5pRTEyMAd1bmlFMTIxB3VuaUUxMjIHdW5pRTEyMwd1bmlFMTI0B3VuaUUxMjUHdW5pRTEyNgd1bmlFMTI3B3VuaUUxMjgHdW5pRTEyOQd1bmlFMTMwB3VuaUUxMzEHdW5pRTEzMgd1bmlFMTMzB3VuaUUxMzQHdW5pRTEzNQd1bmlFMTM2B3VuaUUxMzcHdW5pRTEzOAd1bmlFMTM5B3VuaUUxNDAHdW5pRTE0MQd1bmlFMTQyB3VuaUUxNDMHdW5pRTE0NAd1bmlFMTQ1B3VuaUUxNDYHdW5pRTE0OAd1bmlFMTQ5B3VuaUUxNTAHdW5pRTE1MQd1bmlFMTUyB3VuaUUxNTMHdW5pRTE1NAd1bmlFMTU1B3VuaUUxNTYHdW5pRTE1Nwd1bmlFMTU4B3VuaUUxNTkHdW5pRTE2MAd1bmlFMTYxB3VuaUUxNjIHdW5pRTE2Mwd1bmlFMTY0B3VuaUUxNjUHdW5pRTE2Ngd1bmlFMTY3B3VuaUUxNjgHdW5pRTE2OQd1bmlFMTcwB3VuaUUxNzEHdW5pRTE3Mgd1bmlFMTczB3VuaUUxNzQHdW5pRTE3NQd1bmlFMTc2B3VuaUUxNzcHdW5pRTE3OAd1bmlFMTc5B3VuaUUxODAHdW5pRTE4MQd1bmlFMTgyB3VuaUUxODMHdW5pRTE4NAd1bmlFMTg1B3VuaUUxODYHdW5pRTE4Nwd1bmlFMTg4B3VuaUUxODkHdW5pRTE5MAd1bmlFMTkxB3VuaUUxOTIHdW5pRTE5Mwd1bmlFMTk0B3VuaUUxOTUHdW5pRTE5Nwd1bmlFMTk4B3VuaUUxOTkHdW5pRTIwMAd1bmlFMjAxB3VuaUUyMDIHdW5pRTIwMwd1bmlFMjA0B3VuaUUyMDUHdW5pRTIwNgd1bmlFMjA5B3VuaUUyMTAHdW5pRTIxMQd1bmlFMjEyB3VuaUUyMTMHdW5pRTIxNAd1bmlFMjE1B3VuaUUyMTYHdW5pRTIxOAd1bmlFMjE5B3VuaUUyMjEHdW5pRTIyMwd1bmlFMjI0B3VuaUUyMjUHdW5pRTIyNgd1bmlFMjI3B3VuaUUyMzAHdW5pRTIzMQd1bmlFMjMyB3VuaUUyMzMHdW5pRTIzNAd1bmlFMjM1B3VuaUUyMzYHdW5pRTIzNwd1bmlFMjM4B3VuaUUyMzkHdW5pRTI0MAd1bmlFMjQxB3VuaUUyNDIHdW5pRTI0Mwd1bmlFMjQ0B3VuaUUyNDUHdW5pRTI0Ngd1bmlFMjQ3B3VuaUUyNDgHdW5pRTI0OQd1bmlFMjUwB3VuaUUyNTEHdW5pRTI1Mgd1bmlFMjUzB3VuaUUyNTQHdW5pRTI1NQd1bmlFMjU2B3VuaUUyNTcHdW5pRTI1OAd1bmlFMjU5B3VuaUUyNjAHdW5pRjhGRgZ1MUY1MTEGdTFGNkFBAAAAAAFUUMMXAAA%3D%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIGhvcml6LWFkdi14PSIwIiAvPgo8Z2x5cGggaG9yaXotYWR2LXg9IjQwMCIgLz4KPGdseXBoIHVuaWNvZGU9IiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDE5OyIgZD0iTTYwMCAxMTc0cTMzIDAgNzQgLTVsMzggLTE1Mmw1IC0xcTQ5IC0xNCA5NCAtMzlsNSAtMmwxMzQgODBxNjEgLTQ4IDEwNCAtMTA1bC04MCAtMTM0bDMgLTVxMjUgLTQ0IDM5IC05M2wxIC02bDE1MiAtMzhxNSAtNDMgNSAtNzNxMCAtMzQgLTUgLTc0bC0xNTIgLTM4bC0xIC02cS0xNSAtNDkgLTM5IC05M2wtMyAtNWw4MCAtMTM0cS00OCAtNjEgLTEwNCAtMTA1bC0xMzQgODFsLTUgLTNxLTQ0IC0yNSAtOTQgLTM5bC01IC0ybC0zOCAtMTUxIHEtNDMgLTUgLTc0IC01cS0zMyAwIC03NCA1bC0zOCAxNTFsLTUgMnEtNDkgMTQgLTk0IDM5bC01IDNsLTEzNCAtODFxLTYwIDQ4IC0xMDQgMTA1bDgwIDEzNGwtMyA1cS0yNSA0NSAtMzggOTNsLTIgNmwtMTUxIDM4cS02IDQyIC02IDc0cTAgMzMgNiA3M2wxNTEgMzhsMiA2cTEzIDQ4IDM4IDkzbDMgNWwtODAgMTM0cTQ3IDYxIDEwNSAxMDVsMTMzIC04MGw1IDJxNDUgMjUgOTQgMzlsNSAxbDM4IDE1MnE0MyA1IDc0IDV6TTYwMCA4MTUgcS04OSAwIC0xNTIgLTYzdC02MyAtMTUxLjV0NjMgLTE1MS41dDE1MiAtNjN0MTUyIDYzdDYzIDE1MS41dC02MyAxNTEuNXQtMTUyIDYzeiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMjA7IiBkPSJNNTAwIDEzMDBoMzAwcTQxIDAgNzAuNSAtMjkuNXQyOS41IC03MC41di0xMDBoMjc1cTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtNzVoLTExMDB2NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgyNzV2MTAwcTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNXpNNTAwIDEyMDB2LTEwMGgzMDB2MTAwaC0zMDB6TTExMDAgOTAwdi04MDBxMCAtNDEgLTI5LjUgLTcwLjV0LTcwLjUgLTI5LjVoLTcwMHEtNDEgMCAtNzAuNSAyOS41dC0yOS41IDcwLjUgdjgwMGg5MDB6TTMwMCA4MDB2LTcwMGgxMDB2NzAwaC0xMDB6TTUwMCA4MDB2LTcwMGgxMDB2NzAwaC0xMDB6TTcwMCA4MDB2LTcwMGgxMDB2NzAwaC0xMDB6TTkwMCA4MDB2LTcwMGgxMDB2NzAwaC0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAyMTsiIGQ9Ik0xOCA2MThsNjIwIDYwOHE4IDcgMTguNSA3dDE3LjUgLTdsNjA4IC02MDhxOCAtOCA1LjUgLTEzdC0xMi41IC01aC0xNzV2LTU3NXEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTI1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djM3NWgtMzAwdi0zNzVxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1NzVoLTE3NXEtMTAgMCAtMTIuNSA1dDUuNSAxM3oiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI1OyIgZD0iTTU1MCAxMjAwaDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDUwaDE5MXEyMCAwIDI1LjUgLTExLjV0LTcuNSAtMjcuNWwtMzI3IC00MDBxLTEzIC0xNiAtMzIgLTE2dC0zMiAxNmwtMzI3IDQwMHEtMTMgMTYgLTcuNSAyNy41dDI1LjUgMTEuNWgxOTF2NDUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNMTEyNSA0MDBoNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0zNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41IGgtMTA1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djM1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMTc1aDkwMHYxNzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXoiIC8%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjA2OyIgZD0iTTQ1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNTBoNTBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTEwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMzAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDUwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNODAwIDg1MHYxNTBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTUwMCBoLTIwMHYtMzAwaDIwMHEwIC0zNiAtNyAtNTcuNXQtMjMuNSAtMzB0LTI5LjUgLTEwLjV0LTQwIC0yaC02MDBxLTQxIDAgLTcwLjUgMjkuNXQtMjkuNSA3MC41djgwMHEwIDQxIDI5LjUgNzAuNXQ3MC41IDI5LjV2LTE1MHEwIC02MiA0NCAtMTA2dDEwNiAtNDRoMzAwcTYyIDAgMTA2IDQ0dDQ0IDEwNnpNMTIxMiAyNTBsLTIxMiAtMjEydjE2MmgtMjAwdjEwMGgyMDB2MTYyeiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMDk7IiBkPSJNNjU4IDExOTdsNjM3IC0xMTA0cTIzIC0zOCA3IC02NS41dC02MCAtMjcuNWgtMTI3NnEtNDQgMCAtNjAgMjcuNXQ3IDY1LjVsNjM3IDExMDRxMjIgMzkgNTQgMzl0NTQgLTM5ek03MDQgODAwaC0yMDhxLTIwIDAgLTMyIC0xNC41dC04IC0zNC41bDU4IC0zMDJxNCAtMjAgMjEuNSAtMzQuNXQzNy41IC0xNC41aDU0cTIwIDAgMzcuNSAxNC41dDIxLjUgMzQuNWw1OCAzMDJxNCAyMCAtOCAzNC41dC0zMiAxNC41ek01MDAgMzAwdi0xMDBoMjAwIHYxMDBoLTIwMHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjM1OyIgZD0iTTU1MCAxMTAwcTYyIDAgMTA2IC00NHQ0NCAtMTA2dC00NCAtMTA2dC0xMDYgLTQ0dC0xMDYgNDR0LTQ0IDEwNnQ0NCAxMDZ0MTA2IDQ0ek01NTAgNzAwcTYyIDAgMTA2IC00NHQ0NCAtMTA2dC00NCAtMTA2dC0xMDYgLTQ0dC0xMDYgNDR0LTQ0IDEwNnQ0NCAxMDZ0MTA2IDQ0ek01NTAgMzAwcTYyIDAgMTA2IC00NHQ0NCAtMTA2dC00NCAtMTA2dC0xMDYgLTQ0dC0xMDYgNDR0LTQ0IDEwNnQ0NCAxMDZ0MTA2IDQ0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMzY7IiBkPSJNMTI1IDExMDBoOTUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtOTUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MTUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTEyNSA3MDBoOTUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtOTUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MTUwcTAgMTAgNy41IDE3LjUgdDE3LjUgNy41ek0xMjUgMzAwaDk1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTE1MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTk1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djE1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMzc7IiBkPSJNMzUwIDEyMDBoNTAwcTE2MiAwIDI1NiAtOTMuNXQ5NCAtMjU2LjV2LTUwMHEwIC0xNjUgLTkzLjUgLTI1Ny41dC0yNTYuNSAtOTIuNWgtNTAwcS0xNjUgMCAtMjU3LjUgOTIuNXQtOTIuNSAyNTcuNXY1MDBxMCAxNjUgOTIuNSAyNTcuNXQyNTcuNSA5Mi41ek05MDAgMTAwMGgtNjAwcS00MSAwIC03MC41IC0yOS41dC0yOS41IC03MC41di02MDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDYwMHE0MSAwIDcwLjUgMjkuNSB0MjkuNSA3MC41djYwMHEwIDQxIC0yOS41IDcwLjV0LTcwLjUgMjkuNXpNMzUwIDkwMGg1MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTMwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYzMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek00MDAgODAwdi0yMDBoNDAwdjIwMGgtNDAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyMzg7IiBkPSJNMTUwIDExMDBoMTAwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXQtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNTB2LTIwMGg1MHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXQtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNTB2LTIwMGg1MHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXQtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNTB2LTIwMGg1MHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXQtMTQuNSAtMzUuNSB0LTM1LjUgLTE0LjVoLTEwMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41dDE0LjUgMzUuNXQzNS41IDE0LjVoNTB2MjAwaC01MHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV0MTQuNSAzNS41dDM1LjUgMTQuNWg1MHYyMDBoLTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXQxNC41IDM1LjV0MzUuNSAxNC41aDUwdjIwMGgtNTBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41dDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTIzOTsiIGQ9Ik02NTAgMTE4N3E4NyAtNjcgMTE4LjUgLTE1NnQwIC0xNzh0LTExOC41IC0xNTVxLTg3IDY2IC0xMTguNSAxNTV0MCAxNzh0MTE4LjUgMTU2ek0zMDAgODAwcTEyNCAwIDIxMiAtODh0ODggLTIxMnEtMTI0IDAgLTIxMiA4OHQtODggMjEyek0xMDAwIDgwMHEwIC0xMjQgLTg4IC0yMTJ0LTIxMiAtODhxMCAxMjQgODggMjEydDIxMiA4OHpNMzAwIDUwMHExMjQgMCAyMTIgLTg4dDg4IC0yMTJxLTEyNCAwIC0yMTIgODh0LTg4IDIxMnogTTEwMDAgNTAwcTAgLTEyNCAtODggLTIxMnQtMjEyIC04OHEwIDEyNCA4OCAyMTJ0MjEyIDg4ek03MDAgMTk5di0xNDRxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjV0LTM1LjUgMTQuNXQtMTQuNSAzNS41djE0MnE0MCAtNCA0MyAtNHExNyAwIDU3IDZ6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI0MDsiIGQ9Ik03NDUgODc4bDY5IDE5cTI1IDYgNDUgLTEybDI5OCAtMjk1cTExIC0xMSAxNSAtMjYuNXQtMiAtMzAuNXEtNSAtMTQgLTE4IC0yMy41dC0yOCAtOS41aC04cTEgMCAxIC0xM3EwIC0yOSAtMiAtNTZ0LTguNSAtNjJ0LTIwIC02M3QtMzMgLTUzdC01MSAtMzl0LTcyLjUgLTE0aC0xNDZxLTE4NCAwIC0xODQgMjg4cTAgMjQgMTAgNDdxLTIwIDQgLTYyIDR0LTYzIC00cTExIC0yNCAxMSAtNDdxMCAtMjg4IC0xODQgLTI4OGgtMTQyIHEtNDggMCAtODQuNSAyMXQtNTYgNTF0LTMyIDcxLjV0LTE2IDc1dC0zLjUgNjguNXEwIDEzIDIgMTNoLTdxLTE1IDAgLTI3LjUgOS41dC0xOC41IDIzLjVxLTYgMTUgLTIgMzAuNXQxNSAyNS41bDI5OCAyOTZxMjAgMTggNDYgMTFsNzYgLTE5cTIwIC01IDMwLjUgLTIyLjV0NS41IC0zNy41dC0yMi41IC0zMXQtMzcuNSAtNWwtNTEgMTJsLTE4MiAtMTkzaDg5MWwtMTgyIDE5M2wtNDQgLTEycS0yMCAtNSAtMzcuNSA2dC0yMi41IDMxdDYgMzcuNSB0MzEgMjIuNXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjQ0OyIgZD0iTTEyMDAgMTEwMGgtMTIwMHYxMDBoMTIwMHYtMTAwek01MCAxMDAwaDQwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtOTAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djkwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTY1MCAxMDAwaDQwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDAgcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY0MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek03MDAgOTAwdi0zMDBoMzAwdjMwMGgtMzAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNDU7IiBkPSJNNTAgMTIwMGg0MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTkwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNDAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY5MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek02NTAgNzAwaDQwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djQwMCBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek03MDAgNjAwdi0zMDBoMzAwdjMwMGgtMzAwek0xMjAwIDBoLTEyMDB2MTAwaDEyMDB2LTEwMHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTEwMGgtNDAwdjEwMHE0IDAgMTEgMC41dDI0IDN0MzAgN3QyNCAxNXQxMSAyNC41djU1MGgtMTAwcS0yNSAwIC0zNyAtMC41dC0zMSAtMnQtMjguNSAtNS41dC0yM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i,s,n=t.within,a=n.offset.top+n.scrollTop,o=n.height,h=n.isWindow?n.scrollTop:n.offset.top,l=e.top-t.collisionPosition.marginTop,u=l-h,d=l+t.collisionHeight-o-h,c="top"===t.my[1],p=c?-t.elemHeight:"bottom"===t.my[1]?t.elemHeight:0,f="top"===t.at[1]?t.targetHeight:"bottom"===t.at[1]?-t.targetHeight:0,m=-2*t.offset[1];0>u?(s=e.top+p+f+m+t.collisionHeight-o-a,(0>s||r(u)>s)&&(e.top+=p+f+m)):d>0&&(i=e.top-t.collisionPosition.marginTop+p+f+m-h,(i>0||d>r(i))&&(e.top+=p+f+m))}},flipfit:{left:function(){e.ui.position.flip.left.apply(this,arguments),e.ui.position.fit.left.apply(this,arguments)},top:function(){e.ui.position.flip.top.apply(this,arguments),e.ui.position.fit.top.apply(this,arguments)}}},function(){var t,i,s,n,o,r=document.getElementsByTagName("body")[0],h=document.createElement("div");t=document.createElement(r?"div":"body"),s={visibility:"hidden",width:0,height:0,border:0,margin:0,background:"none"},r&&e.extend(s,{position:"absolute",left:"-1000px",top:"-1000px"});for(o in s)t.style[o]=s[o];t.appendChild(h),i=r||document.documentElement,i.insertBefore(t,i.firstChild),h.style.cssText="position: absolute; left: 10.7432222px;",n=e(h).offset().left,a=n>10&&11>n,t.innerHTML="",i.removeChild(t)}()}(),e.ui.position,e.widget("ui.draggable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this.element.addClass("ui-draggable"),this.options.disabled&&this.element.addClass("ui-draggable-disabled"),this._setHandleClassName(),this._mouseInit()},_setOption:function(e,t){this._super(e,t),"handle"===e&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){return(this.helper||this.element).is(".ui-draggable-dragging")?(this.destroyOnClear=!0,void 0):(this.element.removeClass("ui-draggable ui-draggable-dragging ui-draggable-disabled"),this._removeHandleClassName(),this._mouseDestroy(),void 0)},_mouseCapture:function(t){var i=this.options;return this._blurActiveElement(t),this.helper||i.disabled||e(t.target).closest(".ui-resizable-handle").length>0?!1:(this.handle=this._getHandle(t),this.handle?(this._blockFrames(i.iframeFix===!0?"iframe":i.iframeFix),!0):!1)},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=e(this);return e("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var i=this.document[0];if(this.handleElement.is(t.target))try{i.activeElement&&"body"!==i.activeElement.nodeName.toLowerCase()&&e(i.activeElement).blur()}catch(s){}},_mouseStart:function(t){var i=this.options;return this.helper=this._createHelper(t),this.helper.addClass("ui-draggable-dragging"),this._cacheHelperProportions(),e.ui.ddmanager&&(e.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=this.helper.parents().filter(function(){return"fixed"===e(this).css("position")}).length>0,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger("start",t)===!1?(this._clear(),!1):(this._cacheHelperProportions(),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this._normalizeRightBottom(),this._mouseDrag(t,!0),e.ui.ddmanager&&e.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(e){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:e.pageX-this.offset.left,top:e.pageY-this.offset.top}},_mouseDrag:function(t,i){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!i){var s=this._uiHash();if(this._trigger("drag",t,s)===!1)return this._mouseUp({}),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var i=this,s=!1;return e.ui.ddmanager&&!this.options.dropBehaviour&&(s=e.ui.ddmanager.drop(this,t)),this.dropped&&(s=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!s||"valid"===this.options.revert&&s||this.options.revert===!0||e.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?e(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger("stop",t)!==!1&&i._clear()}):this._trigger("stop",t)!==!1&&this._clear(),!1},_mouseUp:function(t){return this._unblockFrames(),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),this.handleElement.is(t.target)&&this.element.focus(),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){return this.options.handle?!!e(t.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this.handleElement.addClass("ui-draggable-handle")},_removeHandleClassName:function(){this.handleElement.removeClass("ui-draggable-handle")},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper),n=s?e(i.helper.apply(this.element[0],[t])):"clone"===i.helper?this.element.clone().removeAttr("id"):this.element;return n.parents("body").length||n.appendTo("parent"===i.appendTo?this.element[0].parentNode:i.appendTo),s&&n[0]===this.element[0]&&this._setPositionRelative(),n[0]===this.element[0]||/(fixed|absolute)/.test(n.css("position"))||n.css("position","absolute"),n},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(e){return/(html|body)/i.test(e.tagName)||e===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),i=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==i&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var e=this.element.position(),t=this._isRootNode(this.scrollParent[0]);return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+(t?0:this.scrollParent.scrollTop()),left:e.left-(parseInt(this.helper.css("left"),10)||0)+(t?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options,a=this.document[0];return this.relativeContainer=null,n.containment?"window"===n.containment?(this.containment=[e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,e(window).scrollLeft()+e(window).width()-this.helperProportions.width-this.margins.left,e(window).scrollTop()+(e(window).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):"document"===n.containment?(this.containment=[0,0,e(a).width()-this.helperProportions.width-this.margins.left,(e(a).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):("parent"===n.containment&&(n.containment=this.helper[0].parentNode),i=e(n.containment),s=i[0],s&&(t=/(scroll|auto)/.test(i.css("overflow")),this.containment=[(parseInt(i.css("borderLeftWidth"),10)||0)+(parseInt(i.css("paddingLeft"),10)||0),(parseInt(i.css("borderTopWidth"),10)||0)+(parseInt(i.css("paddingTop"),10)||0),(t?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css("borderRightWidth"),10)||0)-(parseInt(i.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css("borderBottomWidth"),10)||0)-(parseInt(i.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=i),void 0):(this.containment=null,void 0)},_convertPositionTo:function(e,t){t||(t=this.position);var i="absolute"===e?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:t.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:t.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(e,t){var i,s,n,a,o=this.options,r=this._isRootNode(this.scrollParent[0]),h=e.pageX,l=e.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),t&&(this.containment&&(this.relativeContainer?(s=this.relativeContainer.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,e.pageX-this.offset.click.left<i[0]&&(h=i[0]+this.offset.click.left),e.pageY-this.offset.click.top<i[1]&&(l=i[1]+this.offset.click.top),e.pageX-this.offset.click.left>i[2]&&(h=i[2]+this.offset.click.left),e.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),o.grid&&(n=o.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/o.grid[1])*o.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-o.grid[1]:n+o.grid[1]:n,a=o.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/o.grid[0])*o.grid[0]:this.originalPageX,h=i?a-this.offset.click.left>=i[0]||a-this.offset.click.left>i[2]?a:a-this.offset.click.left>=i[0]?a-o.grid[0]:a+o.grid[0]:a),"y"===o.axis&&(h=this.originalPageX),"x"===o.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)}},_clear:function(){this.helper.removeClass("ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_normalizeRightBottom:function(){"y"!==this.options.axis&&"auto"!==this.helper.css("right")&&(this.helper.width(this.helper.width()),this.helper.css("right","auto")),"x"!==this.options.axis&&"auto"!==this.helper.css("bottom")&&(this.helper.height(this.helper.height()),this.helper.css("bottom","auto"))},_trigger:function(t,i,s){return s=s||this._uiHash(),e.ui.plugin.call(this,t,[i,s,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),s.offset=this.positionAbs),e.Widget.prototype._trigger.call(this,t,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add("draggable","connectToSortable",{start:function(t,i,s){var n=e.extend({},i,{item:s.element});s.sortables=[],e(s.options.connectToSortable).each(function(){var i=e(this).sortable("instance");i&&!i.options.disabled&&(s.sortables.push(i),i.refreshPositions(),i._trigger("activate",t,n))})},stop:function(t,i,s){var n=e.extend({},i,{item:s.element});s.cancelHelperRemoval=!1,e.each(s.sortables,function(){var e=this;e.isOver?(e.isOver=0,s.cancelHelperRemoval=!0,e.cancelHelperRemoval=!1,e._storedCSS={position:e.placeholder.css("position"),top:e.placeholder.css("top"),left:e.placeholder.css("left")},e._mouseStop(t),e.options.helper=e.options._helper):(e.cancelHelperRemoval=!0,e._trigger("deactivate",t,n))})},drag:function(t,i,s){e.each(s.sortables,function(){var n=!1,a=this;a.positionAbs=s.positionAbs,a.helperProportions=s.helperProportions,a.offset.click=s.offset.click,a._intersectsWith(a.containerCache)&&(n=!0,e.each(s.sortables,function(){return this.positionAbs=s.positionAbs,this.helperProportions=s.helperProportions,this.offset.click=s.offset.click,this!==a&&this._intersectsWith(this.containerCache)&&e.contains(a.element[0],this.element[0])&&(n=!1),n
})),n?(a.isOver||(a.isOver=1,s._parent=i.helper.parent(),a.currentItem=i.helper.appendTo(a.element).data("ui-sortable-item",!0),a.options._helper=a.options.helper,a.options.helper=function(){return i.helper[0]},t.target=a.currentItem[0],a._mouseCapture(t,!0),a._mouseStart(t,!0,!0),a.offset.click.top=s.offset.click.top,a.offset.click.left=s.offset.click.left,a.offset.parent.left-=s.offset.parent.left-a.offset.parent.left,a.offset.parent.top-=s.offset.parent.top-a.offset.parent.top,s._trigger("toSortable",t),s.dropped=a.element,e.each(s.sortables,function(){this.refreshPositions()}),s.currentItem=s.element,a.fromOutside=s),a.currentItem&&(a._mouseDrag(t),i.position=a.position)):a.isOver&&(a.isOver=0,a.cancelHelperRemoval=!0,a.options._revert=a.options.revert,a.options.revert=!1,a._trigger("out",t,a._uiHash(a)),a._mouseStop(t,!0),a.options.revert=a.options._revert,a.options.helper=a.options._helper,a.placeholder&&a.placeholder.remove(),i.helper.appendTo(s._parent),s._refreshOffsets(t),i.position=s._generatePosition(t,!0),s._trigger("fromSortable",t),s.dropped=!1,e.each(s.sortables,function(){this.refreshPositions()}))})}}),e.ui.plugin.add("draggable","cursor",{start:function(t,i,s){var n=e("body"),a=s.options;n.css("cursor")&&(a._cursor=n.css("cursor")),n.css("cursor",a.cursor)},stop:function(t,i,s){var n=s.options;n._cursor&&e("body").css("cursor",n._cursor)}}),e.ui.plugin.add("draggable","opacity",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("opacity")&&(a._opacity=n.css("opacity")),n.css("opacity",a.opacity)},stop:function(t,i,s){var n=s.options;n._opacity&&e(i.helper).css("opacity",n._opacity)}}),e.ui.plugin.add("draggable","scroll",{start:function(e,t,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,i,s){var n=s.options,a=!1,o=s.scrollParentNotHidden[0],r=s.document[0];o!==r&&"HTML"!==o.tagName?(n.axis&&"x"===n.axis||(s.overflowOffset.top+o.offsetHeight-t.pageY<n.scrollSensitivity?o.scrollTop=a=o.scrollTop+n.scrollSpeed:t.pageY-s.overflowOffset.top<n.scrollSensitivity&&(o.scrollTop=a=o.scrollTop-n.scrollSpeed)),n.axis&&"y"===n.axis||(s.overflowOffset.left+o.offsetWidth-t.pageX<n.scrollSensitivity?o.scrollLeft=a=o.scrollLeft+n.scrollSpeed:t.pageX-s.overflowOffset.left<n.scrollSensitivity&&(o.scrollLeft=a=o.scrollLeft-n.scrollSpeed))):(n.axis&&"x"===n.axis||(t.pageY-e(r).scrollTop()<n.scrollSensitivity?a=e(r).scrollTop(e(r).scrollTop()-n.scrollSpeed):e(window).height()-(t.pageY-e(r).scrollTop())<n.scrollSensitivity&&(a=e(r).scrollTop(e(r).scrollTop()+n.scrollSpeed))),n.axis&&"y"===n.axis||(t.pageX-e(r).scrollLeft()<n.scrollSensitivity?a=e(r).scrollLeft(e(r).scrollLeft()-n.scrollSpeed):e(window).width()-(t.pageX-e(r).scrollLeft())<n.scrollSensitivity&&(a=e(r).scrollLeft(e(r).scrollLeft()+n.scrollSpeed)))),a!==!1&&e.ui.ddmanager&&!n.dropBehaviour&&e.ui.ddmanager.prepareOffsets(s,t)}}),e.ui.plugin.add("draggable","snap",{start:function(t,i,s){var n=s.options;s.snapElements=[],e(n.snap.constructor!==String?n.snap.items||":data(ui-draggable)":n.snap).each(function(){var t=e(this),i=t.offset();this!==s.element[0]&&s.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:i.top,left:i.left})})},drag:function(t,i,s){var n,a,o,r,h,l,u,d,c,p,f=s.options,m=f.snapTolerance,g=i.offset.left,v=g+s.helperProportions.width,y=i.offset.top,b=y+s.helperProportions.height;for(c=s.snapElements.length-1;c>=0;c--)h=s.snapElements[c].left-s.margins.left,l=h+s.snapElements[c].width,u=s.snapElements[c].top-s.margins.top,d=u+s.snapElements[c].height,h-m>v||g>l+m||u-m>b||y>d+m||!e.contains(s.snapElements[c].item.ownerDocument,s.snapElements[c].item)?(s.snapElements[c].snapping&&s.options.snap.release&&s.options.snap.release.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=!1):("inner"!==f.snapMode&&(n=m>=Math.abs(u-b),a=m>=Math.abs(d-y),o=m>=Math.abs(h-v),r=m>=Math.abs(l-g),n&&(i.position.top=s._convertPositionTo("relative",{top:u-s.helperProportions.height,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h-s.helperProportions.width}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l}).left)),p=n||a||o||r,"outer"!==f.snapMode&&(n=m>=Math.abs(u-y),a=m>=Math.abs(d-b),o=m>=Math.abs(h-g),r=m>=Math.abs(l-v),n&&(i.position.top=s._convertPositionTo("relative",{top:u,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d-s.helperProportions.height,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l-s.helperProportions.width}).left)),!s.snapElements[c].snapping&&(n||a||o||r||p)&&s.options.snap.snap&&s.options.snap.snap.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=n||a||o||r||p)}}),e.ui.plugin.add("draggable","stack",{start:function(t,i,s){var n,a=s.options,o=e.makeArray(e(a.stack)).sort(function(t,i){return(parseInt(e(t).css("zIndex"),10)||0)-(parseInt(e(i).css("zIndex"),10)||0)});o.length&&(n=parseInt(e(o[0]).css("zIndex"),10)||0,e(o).each(function(t){e(this).css("zIndex",n+t)}),this.css("zIndex",n+o.length))}}),e.ui.plugin.add("draggable","zIndex",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("zIndex")&&(a._zIndex=n.css("zIndex")),n.css("zIndex",a.zIndex)},stop:function(t,i,s){var n=s.options;n._zIndex&&e(i.helper).css("zIndex",n._zIndex)}}),e.ui.draggable,e.widget("ui.droppable",{version:"1.11.4",widgetEventPrefix:"drop",options:{accept:"*",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=e.isFunction(s)?s:function(e){return e.is(s)},this.proportions=function(){return arguments.length?(t=arguments[0],void 0):t?t:t={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this.element.addClass("ui-droppable")},_addToManager:function(t){e.ui.ddmanager.droppables[t]=e.ui.ddmanager.droppables[t]||[],e.ui.ddmanager.droppables[t].push(this)},_splice:function(e){for(var t=0;e.length>t;t++)e[t]===this&&e.splice(t,1)},_destroy:function(){var t=e.ui.ddmanager.droppables[this.options.scope];this._splice(t),this.element.removeClass("ui-droppable ui-droppable-disabled")},_setOption:function(t,i){if("accept"===t)this.accept=e.isFunction(i)?i:function(e){return e.is(i)};else if("scope"===t){var s=e.ui.ddmanager.droppables[this.options.scope];this._splice(s),this._addToManager(i)}this._super(t,i)},_activate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.addClass(this.options.activeClass),i&&this._trigger("activate",t,this.ui(i))},_deactivate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.removeClass(this.options.activeClass),i&&this._trigger("deactivate",t,this.ui(i))},_over:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.addClass(this.options.hoverClass),this._trigger("over",t,this.ui(i)))},_out:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("out",t,this.ui(i)))},_drop:function(t,i){var s=i||e.ui.ddmanager.current,n=!1;return s&&(s.currentItem||s.element)[0]!==this.element[0]?(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var i=e(this).droppable("instance");return i.options.greedy&&!i.options.disabled&&i.options.scope===s.options.scope&&i.accept.call(i.element[0],s.currentItem||s.element)&&e.ui.intersect(s,e.extend(i,{offset:i.element.offset()}),i.options.tolerance,t)?(n=!0,!1):void 0}),n?!1:this.accept.call(this.element[0],s.currentItem||s.element)?(this.options.activeClass&&this.element.removeClass(this.options.activeClass),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("drop",t,this.ui(s)),this.element):!1):!1},ui:function(e){return{draggable:e.currentItem||e.element,helper:e.helper,position:e.position,offset:e.positionAbs}}}),e.ui.intersect=function(){function e(e,t,i){return e>=t&&t+i>e}return function(t,i,s,n){if(!i.offset)return!1;var a=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,r=a+t.helperProportions.width,h=o+t.helperProportions.height,l=i.offset.left,u=i.offset.top,d=l+i.proportions().width,c=u+i.proportions().height;switch(s){case"fit":return a>=l&&d>=r&&o>=u&&c>=h;case"intersect":return a+t.helperProportions.width/2>l&&d>r-t.helperProportions.width/2&&o+t.helperProportions.height/2>u&&c>h-t.helperProportions.height/2;case"pointer":return e(n.pageY,u,i.proportions().height)&&e(n.pageX,l,i.proportions().width);case"touch":return(o>=u&&c>=o||h>=u&&c>=h||u>o&&h>c)&&(a>=l&&d>=a||r>=l&&d>=r||l>a&&r>d);default:return!1}}}(),e.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(t,i){var s,n,a=e.ui.ddmanager.droppables[t.options.scope]||[],o=i?i.type:null,r=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();e:for(s=0;a.length>s;s++)if(!(a[s].options.disabled||t&&!a[s].accept.call(a[s].element[0],t.currentItem||t.element))){for(n=0;r.length>n;n++)if(r[n]===a[s].element[0]){a[s].proportions().height=0;continue e}a[s].visible="none"!==a[s].element.css("display"),a[s].visible&&("mousedown"===o&&a[s]._activate.call(a[s],i),a[s].offset=a[s].element.offset(),a[s].proportions({width:a[s].element[0].offsetWidth,height:a[s].element[0].offsetHeight}))}},drop:function(t,i){var s=!1;return e.each((e.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&e.ui.intersect(t,this,this.options.tolerance,i)&&(s=this._drop.call(this,i)||s),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),s},dragStart:function(t,i){t.element.parentsUntil("body").bind("scroll.droppable",function(){t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)})},drag:function(t,i){t.options.refreshPositions&&e.ui.ddmanager.prepareOffsets(t,i),e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var s,n,a,o=e.ui.intersect(t,this,this.options.tolerance,i),r=!o&&this.isover?"isout":o&&!this.isover?"isover":null;r&&(this.options.greedy&&(n=this.options.scope,a=this.element.parents(":data(ui-droppable)").filter(function(){return e(this).droppable("instance").options.scope===n}),a.length&&(s=e(a[0]).droppable("instance"),s.greedyChild="isover"===r)),s&&"isover"===r&&(s.isover=!1,s.isout=!0,s._out.call(s,i)),this[r]=!0,this["isout"===r?"isover":"isout"]=!1,this["isover"===r?"_over":"_out"].call(this,i),s&&"isout"===r&&(s.isout=!1,s.isover=!0,s._over.call(s,i)))}})},dragStop:function(t,i){t.element.parentsUntil("body").unbind("scroll.droppable"),t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)}},e.ui.droppable,e.widget("ui.resizable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(e){return parseInt(e,10)||0},_isNumber:function(e){return!isNaN(parseInt(e,10))},_hasScroll:function(t,i){if("hidden"===e(t).css("overflow"))return!1;var s=i&&"left"===i?"scrollLeft":"scrollTop",n=!1;return t[s]>0?!0:(t[s]=1,n=t[s]>0,t[s]=0,n)},_create:function(){var t,i,s,n,a,o=this,r=this.options;if(this.element.addClass("ui-resizable"),e.extend(this,{_aspectRatio:!!r.aspectRatio,aspectRatio:r.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:r.helper||r.ghost||r.animate?r.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(e("<div class='ui-wrapper' style='overflow: hidden;'></div>").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css({margin:this.originalElement.css("margin")}),this._proportionallyResize()),this.handles=r.handles||(e(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=e(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),t=this.handles.split(","),this.handles={},i=0;t.length>i;i++)s=e.trim(t[i]),a="ui-resizable-"+s,n=e("<div class='ui-resizable-handle "+a+"'></div>"),n.css({zIndex:r.zIndex}),"se"===s&&n.addClass("ui-icon ui-icon-gripsmall-diagonal-se"),this.handles[s]=".ui-resizable-"+s,this.element.append(n);this._renderAxis=function(t){var i,s,n,a;t=t||this.element;for(i in this.handles)this.handles[i].constructor===String?this.handles[i]=this.element.children(this.handles[i]).first().show():(this.handles[i].jquery||this.handles[i].nodeType)&&(this.handles[i]=e(this.handles[i]),this._on(this.handles[i],{mousedown:o._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(s=e(this.handles[i],this.element),a=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=["padding",/ne|nw|n/.test(i)?"Top":/se|sw|s/.test(i)?"Bottom":/^e$/.test(i)?"Right":"Left"].join(""),t.css(n,a),this._proportionallyResize()),this._handles=this._handles.add(this.handles[i])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.mouseover(function(){o.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),o.axis=n&&n[1]?n[1]:"se")}),r.autoHide&&(this._handles.hide(),e(this.element).addClass("ui-resizable-autohide").mouseenter(function(){r.disabled||(e(this).removeClass("ui-resizable-autohide"),o._handles.show())}).mouseleave(function(){r.disabled||o.resizing||(e(this).addClass("ui-resizable-autohide"),o._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t,i=function(t){e(t).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").removeData("ui-resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};return this.elementIsWrapper&&(i(this.element),t=this.element,this.originalElement.css({position:t.css("position"),width:t.outerWidth(),height:t.outerHeight(),top:t.css("top"),left:t.css("left")}).insertAfter(t),t.remove()),this.originalElement.css("resize",this.originalResizeStyle),i(this.originalElement),this},_mouseCapture:function(t){var i,s,n=!1;for(i in this.handles)s=e(this.handles[i])[0],(s===t.target||e.contains(s,t.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var i,s,n,a=this.options,o=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css("left")),s=this._num(this.helper.css("top")),a.containment&&(i+=e(a.containment).scrollLeft()||0,s+=e(a.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:o.width(),height:o.height()},this.originalSize=this._helper?{width:o.outerWidth(),height:o.outerHeight()}:{width:o.width(),height:o.height()},this.sizeDiff={width:o.outerWidth()-o.width(),height:o.outerHeight()-o.height()},this.originalPosition={left:i,top:s},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof a.aspectRatio?a.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=e(".ui-resizable-"+this.axis).css("cursor"),e("body").css("cursor","auto"===n?this.axis+"-resize":n),o.addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var i,s,n=this.originalMousePosition,a=this.axis,o=t.pageX-n.left||0,r=t.pageY-n.top||0,h=this._change[a];return this._updatePrevProperties(),h?(i=h.apply(this,[t,o,r]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(i=this._updateRatio(i,t)),i=this._respectSize(i,t),this._updateCache(i),this._propagate("resize",t),s=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),e.isEmptyObject(s)||(this._updatePrevProperties(),this._trigger("resize",t,this.ui()),this._applyChanges()),!1):!1},_mouseStop:function(t){this.resizing=!1;var i,s,n,a,o,r,h,l=this.options,u=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],"left")?0:u.sizeDiff.height,a=s?0:u.sizeDiff.width,o={width:u.helper.width()-a,height:u.helper.height()-n},r=parseInt(u.element.css("left"),10)+(u.position.left-u.originalPosition.left)||null,h=parseInt(u.element.css("top"),10)+(u.position.top-u.originalPosition.top)||null,l.animate||this.element.css(e.extend(o,{top:h,left:r})),u.helper.height(u.size.height),u.helper.width(u.size.width),this._helper&&!l.animate&&this._proportionallyResize()),e("body").css("cursor","auto"),this.element.removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var e={};return this.position.top!==this.prevPosition.top&&(e.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(e.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(e.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(e.height=this.size.height+"px"),this.helper.css(e),e},_updateVirtualBoundaries:function(e){var t,i,s,n,a,o=this.options;a={minWidth:this._isNumber(o.minWidth)?o.minWidth:0,maxWidth:this._isNumber(o.maxWidth)?o.maxWidth:1/0,minHeight:this._isNumber(o.minHeight)?o.minHeight:0,maxHeight:this._isNumber(o.maxHeight)?o.maxHeight:1/0},(this._aspectRatio||e)&&(t=a.minHeight*this.aspectRatio,s=a.minWidth/this.aspectRatio,i=a.maxHeight*this.aspectRatio,n=a.maxWidth/this.aspectRatio,t>a.minWidth&&(a.minWidth=t),s>a.minHeight&&(a.minHeight=s),a.maxWidth>i&&(a.maxWidth=i),a.maxHeight>n&&(a.maxHeight=n)),this._vBoundaries=a},_updateCache:function(e){this.offset=this.helper.offset(),this._isNumber(e.left)&&(this.position.left=e.left),this._isNumber(e.top)&&(this.position.top=e.top),this._isNumber(e.height)&&(this.size.height=e.height),this._isNumber(e.width)&&(this.size.width=e.width)},_updateRatio:function(e){var t=this.position,i=this.size,s=this.axis;return this._isNumber(e.height)?e.width=e.height*this.aspectRatio:this._isNumber(e.width)&&(e.height=e.width/this.aspectRatio),"sw"===s&&(e.left=t.left+(i.width-e.width),e.top=null),"nw"===s&&(e.top=t.top+(i.height-e.height),e.left=t.left+(i.width-e.width)),e},_respectSize:function(e){var t=this._vBoundaries,i=this.axis,s=this._isNumber(e.width)&&t.maxWidth&&t.maxWidth<e.width,n=this._isNumber(e.height)&&t.maxHeight&&t.maxHeight<e.height,a=this._isNumber(e.width)&&t.minWidth&&t.minWidth>e.width,o=this._isNumber(e.height)&&t.minHeight&&t.minHeight>e.height,r=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,l=/sw|nw|w/.test(i),u=/nw|ne|n/.test(i);return a&&(e.width=t.minWidth),o&&(e.height=t.minHeight),s&&(e.width=t.maxWidth),n&&(e.height=t.maxHeight),a&&l&&(e.left=r-t.minWidth),s&&l&&(e.left=r-t.maxWidth),o&&u&&(e.top=h-t.minHeight),n&&u&&(e.top=h-t.maxHeight),e.width||e.height||e.left||!e.top?e.width||e.height||e.top||!e.left||(e.left=null):e.top=null,e},_getPaddingPlusBorderDimensions:function(e){for(var t=0,i=[],s=[e.css("borderTopWidth"),e.css("borderRightWidth"),e.css("borderBottomWidth"),e.css("borderLeftWidth")],n=[e.css("paddingTop"),e.css("paddingRight"),e.css("paddingBottom"),e.css("paddingLeft")];4>t;t++)i[t]=parseInt(s[t],10)||0,i[t]+=parseInt(n[t],10)||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var e,t=0,i=this.helper||this.element;this._proportionallyResizeElements.length>t;t++)e=this._proportionallyResizeElements[t],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(e)),e.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var t=this.element,i=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||e("<div style='overflow:hidden;'></div>"),this.helper.addClass(this._helper).css({width:this.element.outerWidth()-1,height:this.element.outerHeight()-1,position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++i.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(e,t){return{width:this.originalSize.width+t}},w:function(e,t){var i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(e,t,i){return{height:this.originalSize.height+i}},se:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},sw:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,i,s]))},ne:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},nw:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,i,s]))}},_propagate:function(t,i){e.ui.plugin.call(this,t,[i,this.ui()]),"resize"!==t&&this._trigger(t,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),e.ui.plugin.add("resizable","animate",{stop:function(t){var i=e(this).resizable("instance"),s=i.options,n=i._proportionallyResizeElements,a=n.length&&/textarea/i.test(n[0].nodeName),o=a&&i._hasScroll(n[0],"left")?0:i.sizeDiff.height,r=a?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-o},l=parseInt(i.element.css("left"),10)+(i.position.left-i.originalPosition.left)||null,u=parseInt(i.element.css("top"),10)+(i.position.top-i.originalPosition.top)||null;i.element.animate(e.extend(h,u&&l?{top:u,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseInt(i.element.css("width"),10),height:parseInt(i.element.css("height"),10),top:parseInt(i.element.css("top"),10),left:parseInt(i.element.css("left"),10)};n&&n.length&&e(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate("resize",t)}})}}),e.ui.plugin.add("resizable","containment",{start:function(){var t,i,s,n,a,o,r,h=e(this).resizable("instance"),l=h.options,u=h.element,d=l.containment,c=d instanceof e?d.get(0):/parent/.test(d)?u.parent().get(0):d;c&&(h.containerElement=e(c),/document/.test(d)||d===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight}):(t=e(c),i=[],e(["Top","Right","Left","Bottom"]).each(function(e,s){i[e]=h._num(t.css("padding"+s))}),h.containerOffset=t.offset(),h.containerPosition=t.position(),h.containerSize={height:t.innerHeight()-i[3],width:t.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,a=h.containerSize.width,o=h._hasScroll(c,"left")?c.scrollWidth:a,r=h._hasScroll(c)?c.scrollHeight:n,h.parentData={element:c,left:s.left,top:s.top,width:o,height:r}))},resize:function(t){var i,s,n,a,o=e(this).resizable("instance"),r=o.options,h=o.containerOffset,l=o.position,u=o._aspectRatio||t.shiftKey,d={top:0,left:0},c=o.containerElement,p=!0;c[0]!==document&&/static/.test(c.css("position"))&&(d=h),l.left<(o._helper?h.left:0)&&(o.size.width=o.size.width+(o._helper?o.position.left-h.left:o.position.left-d.left),u&&(o.size.height=o.size.width/o.aspectRatio,p=!1),o.position.left=r.helper?h.left:0),l.top<(o._helper?h.top:0)&&(o.size.height=o.size.height+(o._helper?o.position.top-h.top:o.position.top),u&&(o.size.width=o.size.height*o.aspectRatio,p=!1),o.position.top=o._helper?h.top:0),n=o.containerElement.get(0)===o.element.parent().get(0),a=/relative|absolute/.test(o.containerElement.css("position")),n&&a?(o.offset.left=o.parentData.left+o.position.left,o.offset.top=o.parentData.top+o.position.top):(o.offset.left=o.element.offset().left,o.offset.top=o.element.offset().top),i=Math.abs(o.sizeDiff.width+(o._helper?o.offset.left-d.left:o.offset.left-h.left)),s=Math.abs(o.sizeDiff.height+(o._helper?o.offset.top-d.top:o.offset.top-h.top)),i+o.size.width>=o.parentData.width&&(o.size.width=o.parentData.width-i,u&&(o.size.height=o.size.width/o.aspectRatio,p=!1)),s+o.size.height>=o.parentData.height&&(o.size.height=o.parentData.height-s,u&&(o.size.width=o.size.height*o.aspectRatio,p=!1)),p||(o.position.left=o.prevPosition.left,o.position.top=o.prevPosition.top,o.size.width=o.prevSize.width,o.size.height=o.prevSize.height)},stop:function(){var t=e(this).resizable("instance"),i=t.options,s=t.containerOffset,n=t.containerPosition,a=t.containerElement,o=e(t.helper),r=o.offset(),h=o.outerWidth()-t.sizeDiff.width,l=o.outerHeight()-t.sizeDiff.height;t._helper&&!i.animate&&/relative/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l}),t._helper&&!i.animate&&/static/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),e.ui.plugin.add("resizable","alsoResize",{start:function(){var t=e(this).resizable("instance"),i=t.options;e(i.alsoResize).each(function(){var t=e(this);t.data("ui-resizable-alsoresize",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css("left"),10),top:parseInt(t.css("top"),10)})})},resize:function(t,i){var s=e(this).resizable("instance"),n=s.options,a=s.originalSize,o=s.originalPosition,r={height:s.size.height-a.height||0,width:s.size.width-a.width||0,top:s.position.top-o.top||0,left:s.position.left-o.left||0};e(n.alsoResize).each(function(){var t=e(this),s=e(this).data("ui-resizable-alsoresize"),n={},a=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];e.each(a,function(e,t){var i=(s[t]||0)+(r[t]||0);i&&i>=0&&(n[t]=i||null)}),t.css(n)})},stop:function(){e(this).removeData("resizable-alsoresize")}}),e.ui.plugin.add("resizable","ghost",{start:function(){var t=e(this).resizable("instance"),i=t.options,s=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass("ui-resizable-ghost").addClass("string"==typeof i.ghost?i.ghost:""),t.ghost.appendTo(t.helper)},resize:function(){var t=e(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=e(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),e.ui.plugin.add("resizable","grid",{resize:function(){var t,i=e(this).resizable("instance"),s=i.options,n=i.size,a=i.originalSize,o=i.originalPosition,r=i.axis,h="number"==typeof s.grid?[s.grid,s.grid]:s.grid,l=h[0]||1,u=h[1]||1,d=Math.round((n.width-a.width)/l)*l,c=Math.round((n.height-a.height)/u)*u,p=a.width+d,f=a.height+c,m=s.maxWidth&&p>s.maxWidth,g=s.maxHeight&&f>s.maxHeight,v=s.minWidth&&s.minWidth>p,y=s.minHeight&&s.minHeight>f;s.grid=h,v&&(p+=l),y&&(f+=u),m&&(p-=l),g&&(f-=u),/^(se|s|e)$/.test(r)?(i.size.width=p,i.size.height=f):/^(ne)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.top=o.top-c):/^(sw)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.left=o.left-d):((0>=f-u||0>=p-l)&&(t=i._getPaddingPlusBorderDimensions(this)),f-u>0?(i.size.height=f,i.position.top=o.top-c):(f=u-t.height,i.size.height=f,i.position.top=o.top+a.height-f),p-l>0?(i.size.width=p,i.position.left=o.left-d):(p=l-t.width,i.size.width=p,i.position.left=o.left+a.width-p))}}),e.ui.resizable,e.widget("ui.selectable",e.ui.mouse,{version:"1.11.4",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var t,i=this;this.element.addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){t=e(i.options.filter,i.element[0]),t.addClass("ui-selectee"),t.each(function(){var t=e(this),i=t.offset();e.data(this,"selectable-item",{element:this,$element:t,left:i.left,top:i.top,right:i.left+t.outerWidth(),bottom:i.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this.selectees=t.addClass("ui-selectee"),this._mouseInit(),this.helper=e("<div class='ui-selectable-helper'></div>")},_destroy:function(){this.selectees.removeClass("ui-selectee").removeData("selectable-item"),this.element.removeClass("ui-selectable ui-selectable-disabled"),this._mouseDestroy()},_mouseStart:function(t){var i=this,s=this.options;this.opos=[t.pageX,t.pageY],this.options.disabled||(this.selectees=e(s.filter,this.element[0]),this._trigger("start",t),e(s.appendTo).append(this.helper),this.helper.css({left:t.pageX,top:t.pageY,width:0,height:0}),s.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var s=e.data(this,"selectable-item");s.startselected=!0,t.metaKey||t.ctrlKey||(s.$element.removeClass("ui-selected"),s.selected=!1,s.$element.addClass("ui-unselecting"),s.unselecting=!0,i._trigger("unselecting",t,{unselecting:s.element}))}),e(t.target).parents().addBack().each(function(){var s,n=e.data(this,"selectable-item");return n?(s=!t.metaKey&&!t.ctrlKey||!n.$element.hasClass("ui-selected"),n.$element.removeClass(s?"ui-unselecting":"ui-selected").addClass(s?"ui-selecting":"ui-unselecting"),n.unselecting=!s,n.selecting=s,n.selected=s,s?i._trigger("selecting",t,{selecting:n.element}):i._trigger("unselecting",t,{unselecting:n.element}),!1):void 0}))},_mouseDrag:function(t){if(this.dragged=!0,!this.options.disabled){var i,s=this,n=this.options,a=this.opos[0],o=this.opos[1],r=t.pageX,h=t.pageY;return a>r&&(i=r,r=a,a=i),o>h&&(i=h,h=o,o=i),this.helper.css({left:a,top:o,width:r-a,height:h-o}),this.selectees.each(function(){var i=e.data(this,"selectable-item"),l=!1;
i&&i.element!==s.element[0]&&("touch"===n.tolerance?l=!(i.left>r||a>i.right||i.top>h||o>i.bottom):"fit"===n.tolerance&&(l=i.left>a&&r>i.right&&i.top>o&&h>i.bottom),l?(i.selected&&(i.$element.removeClass("ui-selected"),i.selected=!1),i.unselecting&&(i.$element.removeClass("ui-unselecting"),i.unselecting=!1),i.selecting||(i.$element.addClass("ui-selecting"),i.selecting=!0,s._trigger("selecting",t,{selecting:i.element}))):(i.selecting&&((t.metaKey||t.ctrlKey)&&i.startselected?(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.$element.addClass("ui-selected"),i.selected=!0):(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.startselected&&(i.$element.addClass("ui-unselecting"),i.unselecting=!0),s._trigger("unselecting",t,{unselecting:i.element}))),i.selected&&(t.metaKey||t.ctrlKey||i.startselected||(i.$element.removeClass("ui-selected"),i.selected=!1,i.$element.addClass("ui-unselecting"),i.unselecting=!0,s._trigger("unselecting",t,{unselecting:i.element})))))}),!1}},_mouseStop:function(t){var i=this;return this.dragged=!1,e(".ui-unselecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-unselecting"),s.unselecting=!1,s.startselected=!1,i._trigger("unselected",t,{unselected:s.element})}),e(".ui-selecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-selecting").addClass("ui-selected"),s.selecting=!1,s.selected=!0,s.startselected=!0,i._trigger("selected",t,{selected:s.element})}),this._trigger("stop",t),this.helper.remove(),!1}}),e.widget("ui.sortable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(e,t,i){return e>=t&&t+i>e},_isFloating:function(e){return/left|right/.test(e.css("float"))||/inline|table-cell/.test(e.css("display"))},_create:function(){this.containerCache={},this.element.addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(e,t){this._super(e,t),"handle"===e&&this._setHandleClassName()},_setHandleClassName:function(){this.element.find(".ui-sortable-handle").removeClass("ui-sortable-handle"),e.each(this.items,function(){(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item).addClass("ui-sortable-handle")})},_destroy:function(){this.element.removeClass("ui-sortable ui-sortable-disabled").find(".ui-sortable-handle").removeClass("ui-sortable-handle"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,i){var s=null,n=!1,a=this;return this.reverting?!1:this.options.disabled||"static"===this.options.type?!1:(this._refreshItems(t),e(t.target).parents().each(function(){return e.data(this,a.widgetName+"-item")===a?(s=e(this),!1):void 0}),e.data(t.target,a.widgetName+"-item")===a&&(s=e(t.target)),s?!this.options.handle||i||(e(this.options.handle,s).find("*").addBack().each(function(){this===t.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(t,i,s){var n,a,o=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(a=this.document.find("body"),this.storedCursor=a.css("cursor"),a.css("cursor",o.cursor),this.storedStylesheet=e("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(a)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger("activate",t,this._uiHash(this));return e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass("ui-sortable-helper"),this._mouseDrag(t),!0},_mouseDrag:function(t){var i,s,n,a,o=this.options,r=!1;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<o.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+o.scrollSpeed:t.pageY-this.overflowOffset.top<o.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-o.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<o.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+o.scrollSpeed:t.pageX-this.overflowOffset.left<o.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-o.scrollSpeed)):(t.pageY-this.document.scrollTop()<o.scrollSensitivity?r=this.document.scrollTop(this.document.scrollTop()-o.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<o.scrollSensitivity&&(r=this.document.scrollTop(this.document.scrollTop()+o.scrollSpeed)),t.pageX-this.document.scrollLeft()<o.scrollSensitivity?r=this.document.scrollLeft(this.document.scrollLeft()-o.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<o.scrollSensitivity&&(r=this.document.scrollLeft(this.document.scrollLeft()+o.scrollSpeed))),r!==!1&&e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t)),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),i=this.items.length-1;i>=0;i--)if(s=this.items[i],n=s.item[0],a=this._intersectsWithPointer(s),a&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===a?"next":"prev"]()[0]!==n&&!e.contains(this.placeholder[0],n)&&("semi-dynamic"===this.options.type?!e.contains(this.element[0],n):!0)){if(this.direction=1===a?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,i){if(t){if(e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t),this.options.revert){var s=this,n=this.placeholder.offset(),a=this.options.axis,o={};a&&"x"!==a||(o.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),a&&"y"!==a||(o.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,e(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){s._clear(t)})}else this._clear(t,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp({target:null}),"original"===this.options.helper?this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},e(i).each(function(){var i=(e(t.item||this).attr(t.attribute||"id")||"").match(t.expression||/(.+)[\-=_](.+)/);i&&s.push((t.key||i[1]+"[]")+"="+(t.key&&t.expression?i[1]:i[2]))}),!s.length&&t.key&&s.push(t.key+"="),s.join("&")},toArray:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},i.each(function(){s.push(e(t.item||this).attr(t.attribute||"id")||"")}),s},_intersectsWith:function(e){var t=this.positionAbs.left,i=t+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,a=e.left,o=a+e.width,r=e.top,h=r+e.height,l=this.offset.click.top,u=this.offset.click.left,d="x"===this.options.axis||s+l>r&&h>s+l,c="y"===this.options.axis||t+u>a&&o>t+u,p=d&&c;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>e[this.floating?"width":"height"]?p:t+this.helperProportions.width/2>a&&o>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(e){var t="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top,e.height),i="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left,e.width),s=t&&i,n=this._getDragVerticalDirection(),a=this._getDragHorizontalDirection();return s?this.floating?a&&"right"===a||"down"===n?2:1:n&&("down"===n?2:1):!1},_intersectsWithSides:function(e){var t=this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top+e.height/2,e.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left+e.width/2,e.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?"right"===n&&i||"left"===n&&!i:s&&("down"===s&&t||"up"===s&&!t)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return 0!==e&&(e>0?"down":"up")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return 0!==e&&(e>0?"right":"left")},refresh:function(e){return this._refreshItems(e),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor===String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){function i(){r.push(this)}var s,n,a,o,r=[],h=[],l=this._connectWith();if(l&&t)for(s=l.length-1;s>=0;s--)for(a=e(l[s],this.document[0]),n=a.length-1;n>=0;n--)o=e.data(a[n],this.widgetFullName),o&&o!==this&&!o.options.disabled&&h.push([e.isFunction(o.options.items)?o.options.items.call(o.element):e(o.options.items,o.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),o]);for(h.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return e(r)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=e.grep(this.items,function(e){for(var i=0;t.length>i;i++)if(t[i]===e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var i,s,n,a,o,r,h,l,u=this.items,d=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],c=this._connectWith();if(c&&this.ready)for(i=c.length-1;i>=0;i--)for(n=e(c[i],this.document[0]),s=n.length-1;s>=0;s--)a=e.data(n[s],this.widgetFullName),a&&a!==this&&!a.options.disabled&&(d.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a));for(i=d.length-1;i>=0;i--)for(o=d[i][1],r=d[i][0],s=0,l=r.length;l>s;s++)h=e(r[s]),h.data(this.widgetName+"-item",o),u.push({item:h,instance:o,width:0,height:0,left:0,top:0})},refreshPositions:function(t){this.floating=this.items.length?"x"===this.options.axis||this._isFloating(this.items[0].item):!1,this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,a;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?e(this.options.toleranceElement,s.item):s.item,t||(s.width=n.outerWidth(),s.height=n.outerHeight()),a=n.offset(),s.left=a.left,s.top=a.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)a=this.containers[i].element.offset(),this.containers[i].containerCache.left=a.left,this.containers[i].containerCache.top=a.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(t){t=t||this;var i,s=t.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=t.currentItem[0].nodeName.toLowerCase(),n=e("<"+s+">",t.document[0]).addClass(i||t.currentItem[0].className+" ui-sortable-placeholder").removeClass("ui-sortable-helper");return"tbody"===s?t._createTrPlaceholder(t.currentItem.find("tr").eq(0),e("<tr>",t.document[0]).appendTo(n)):"tr"===s?t._createTrPlaceholder(t.currentItem,n):"img"===s&&n.attr("src",t.currentItem.attr("src")),i||n.css("visibility","hidden"),n},update:function(e,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css("paddingTop")||0,10)-parseInt(t.currentItem.css("paddingBottom")||0,10)),n.width()||n.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css("paddingLeft")||0,10)-parseInt(t.currentItem.css("paddingRight")||0,10)))}}),t.placeholder=e(s.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),s.placeholder.update(t,t.placeholder)},_createTrPlaceholder:function(t,i){var s=this;t.children().each(function(){e("<td>&#160;</td>",s.document[0]).attr("colspan",e(this).attr("colspan")||1).appendTo(i)})},_contactContainers:function(t){var i,s,n,a,o,r,h,l,u,d,c=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!e.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(c&&e.contains(this.containers[i].element[0],c.element[0]))continue;c=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",t,this._uiHash(this)),this.containers[i].containerCache.over=0);if(c)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,a=null,u=c.floating||this._isFloating(this.currentItem),o=u?"left":"top",r=u?"width":"height",d=u?"clientX":"clientY",s=this.items.length-1;s>=0;s--)e.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[o],l=!1,t[d]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(t[d]-h)&&(n=Math.abs(t[d]-h),a=this.items[s],this.direction=l?"up":"down"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return this.currentContainer.containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1),void 0;a?this._rearrange(t,a,null,!0):this._rearrange(t,null,this.containers[p].element,!0),this._trigger("change",t,this._uiHash()),this.containers[p]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return s.parents("body").length||e("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&e.ui.ie)&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var e=this.currentItem.position();return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options;"parent"===n.containment&&(n.containment=this.helper[0].parentNode),("document"===n.containment||"window"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===n.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===n.containment?this.document.width():this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(t=e(n.containment)[0],i=e(n.containment).offset(),s="hidden"!==e(t).css("overflow"),this.containment=[i.left+(parseInt(e(t).css("borderLeftWidth"),10)||0)+(parseInt(e(t).css("paddingLeft"),10)||0)-this.margins.left,i.top+(parseInt(e(t).css("borderTopWidth"),10)||0)+(parseInt(e(t).css("paddingTop"),10)||0)-this.margins.top,i.left+(s?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(e(t).css("borderLeftWidth"),10)||0)-(parseInt(e(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(e(t).css("borderTopWidth"),10)||0)-(parseInt(e(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,i){i||(i=this.position);var s="absolute"===t?1:-1,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,a=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():a?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():a?0:n.scrollLeft())*s}},_generatePosition:function(t){var i,s,n=this.options,a=t.pageX,o=t.pageY,r="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(a=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(o=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(a=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(o=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1],o=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((a-this.originalPageX)/n.grid[0])*n.grid[0],a=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:a-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(e,t,i,s){i?i[0].appendChild(this.placeholder[0]):t.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?t.item[0]:t.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(e,t){function i(e,t,i){return function(s){i._trigger(e,s,t._uiHash(t))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)("auto"===this._storedCSS[s]||"static"===this._storedCSS[s])&&(this._storedCSS[s]="");this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper")}else this.currentItem.show();for(this.fromOutside&&!t&&n.push(function(e){this._trigger("receive",e,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||t||n.push(function(e){this._trigger("update",e,this._uiHash())}),this!==this.currentContainer&&(t||(n.push(function(e){this._trigger("remove",e,this._uiHash())}),n.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)t||n.push(i("deactivate",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i("out",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,t||this._trigger("beforeStop",e,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!t){for(s=0;n.length>s;s++)n[s].call(this,e);this._trigger("stop",e,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){e.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(t){var i=t||this;return{helper:i.helper,placeholder:i.placeholder||e([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:t?t.element:null}}}),e.widget("ui.accordion",{version:"1.11.4",options:{active:0,animate:{},collapsible:!1,event:"click",header:"> li > :first-child,> :not(li):even",heightStyle:"auto",icons:{activeHeader:"ui-icon-triangle-1-s",header:"ui-icon-triangle-1-e"},activate:null,beforeActivate:null},hideProps:{borderTopWidth:"hide",borderBottomWidth:"hide",paddingTop:"hide",paddingBottom:"hide",height:"hide"},showProps:{borderTopWidth:"show",borderBottomWidth:"show",paddingTop:"show",paddingBottom:"show",height:"show"},_create:function(){var t=this.options;this.prevShow=this.prevHide=e(),this.element.addClass("ui-accordion ui-widget ui-helper-reset").attr("role","tablist"),t.collapsible||t.active!==!1&&null!=t.active||(t.active=0),this._processPanels(),0>t.active&&(t.active+=this.headers.length),this._refresh()},_getCreateEventData:function(){return{header:this.active,panel:this.active.length?this.active.next():e()}},_createIcons:function(){var t=this.options.icons;t&&(e("<span>").addClass("ui-accordion-header-icon ui-icon "+t.header).prependTo(this.headers),this.active.children(".ui-accordion-header-icon").removeClass(t.header).addClass(t.activeHeader),this.headers.addClass("ui-accordion-icons"))},_destroyIcons:function(){this.headers.removeClass("ui-accordion-icons").children(".ui-accordion-header-icon").remove()},_destroy:function(){var e;this.element.removeClass("ui-accordion ui-widget ui-helper-reset").removeAttr("role"),this.headers.removeClass("ui-accordion-header ui-accordion-header-active ui-state-default ui-corner-all ui-state-active ui-state-disabled ui-corner-top").removeAttr("role").removeAttr("aria-expanded").removeAttr("aria-selected").removeAttr("aria-controls").removeAttr("tabIndex").removeUniqueId(),this._destroyIcons(),e=this.headers.next().removeClass("ui-helper-reset ui-widget-content ui-corner-bottom ui-accordion-content ui-accordion-content-active ui-state-disabled").css("display","").removeAttr("role").removeAttr("aria-hidden").removeAttr("aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&e.css("height","")},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):("event"===e&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(t)),this._super(e,t),"collapsible"!==e||t||this.options.active!==!1||this._activate(0),"icons"===e&&(this._destroyIcons(),t&&this._createIcons()),"disabled"===e&&(this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this.headers.add(this.headers.next()).toggleClass("ui-state-disabled",!!t)),void 0)},_keydown:function(t){if(!t.altKey&&!t.ctrlKey){var i=e.ui.keyCode,s=this.headers.length,n=this.headers.index(t.target),a=!1;switch(t.keyCode){case i.RIGHT:case i.DOWN:a=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:a=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(t);break;case i.HOME:a=this.headers[0];break;case i.END:a=this.headers[s-1]}a&&(e(t.target).attr("tabIndex",-1),e(a).attr("tabIndex",0),a.focus(),t.preventDefault())}},_panelKeyDown:function(t){t.keyCode===e.ui.keyCode.UP&&t.ctrlKey&&e(t.currentTarget).prev().focus()},refresh:function(){var t=this.options;this._processPanels(),t.active===!1&&t.collapsible===!0||!this.headers.length?(t.active=!1,this.active=e()):t.active===!1?this._activate(0):this.active.length&&!e.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(t.active=!1,this.active=e()):this._activate(Math.max(0,t.active-1)):t.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var e=this.headers,t=this.panels;this.headers=this.element.find(this.options.header).addClass("ui-accordion-header ui-state-default ui-corner-all"),this.panels=this.headers.next().addClass("ui-accordion-content ui-helper-reset ui-widget-content ui-corner-bottom").filter(":not(.ui-accordion-content-active)").hide(),t&&(this._off(e.not(this.headers)),this._off(t.not(this.panels)))},_refresh:function(){var 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i=t.newPanel,s=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,t):(s.hide(),i.show(),this._toggleComplete(t)),s.attr({"aria-hidden":"true"}),s.prev().attr({"aria-selected":"false","aria-expanded":"false"}),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===parseInt(e(this).attr("tabIndex"),10)}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(e,t,i){var s,n,a,o=this,r=0,h=e.css("box-sizing"),l=e.length&&(!t.length||e.index()<t.index()),u=this.options.animate||{},d=l&&u.down||u,c=function(){o._toggleComplete(i)};return"number"==typeof d&&(a=d),"string"==typeof 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e.ui.keyCode.LEFT:this.collapse(t);break;case e.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case e.ui.keyCode.ENTER:case e.ui.keyCode.SPACE:this._activate(t);break;case e.ui.keyCode.ESCAPE:this.collapse(t);break;default:o=!1,s=this.previousFilter||"",n=String.fromCharCode(t.keyCode),a=!1,clearTimeout(this.filterTimer),n===s?a=!0:n=s+n,i=this._filterMenuItems(n),i=a&&-1!==i.index(this.active.next())?this.active.nextAll(".ui-menu-item"):i,i.length||(n=String.fromCharCode(t.keyCode),i=this._filterMenuItems(n)),i.length?(this.focus(t,i),this.previousFilter=n,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}o&&t.preventDefault()},_activate:function(e){this.active.is(".ui-state-disabled")||(this.active.is("[aria-haspopup='true']")?this.expand(e):this.select(e))},refresh:function(){var t,i,s=this,n=this.options.icons.submenu,a=this.element.find(this.options.menus);this.element.toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length),a.filter(":not(.ui-menu)").addClass("ui-menu ui-widget ui-widget-content ui-front").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=e(this),i=t.parent(),s=e("<span>").addClass("ui-menu-icon ui-icon "+n).data("ui-menu-submenu-carat",!0);i.attr("aria-haspopup","true").prepend(s),t.attr("aria-labelledby",i.attr("id"))}),t=a.add(this.element),i=t.find(this.options.items),i.not(".ui-menu-item").each(function(){var t=e(this);s._isDivider(t)&&t.addClass("ui-widget-content ui-menu-divider")}),i.not(".ui-menu-item, 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i,s;this.blur(e,e&&"focus"===e.type),this._scrollIntoView(t),this.active=t.first(),s=this.active.addClass("ui-state-focus").removeClass("ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",s.attr("id")),this.active.parent().closest(".ui-menu-item").addClass("ui-state-active"),e&&"keydown"===e.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=t.children(".ui-menu"),i.length&&e&&/^mouse/.test(e.type)&&this._startOpening(i),this.activeMenu=t.parent(),this._trigger("focus",e,{item:t})},_scrollIntoView:function(t){var i,s,n,a,o,r;this._hasScroll()&&(i=parseFloat(e.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(e.css(this.activeMenu[0],"paddingTop"))||0,n=t.offset().top-this.activeMenu.offset().top-i-s,a=this.activeMenu.scrollTop(),o=this.activeMenu.height(),r=t.outerHeight(),0>n?this.activeMenu.scrollTop(a+n):n+r>o&&this.activeMenu.scrollTop(a+n-o+r))},blur:function(e,t){t||clearTimeout(this.timer),this.active&&(this.active.removeClass("ui-state-focus"),this.active=null,this._trigger("blur",e,{item:this.active}))},_startOpening:function(e){clearTimeout(this.timer),"true"===e.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(e)},this.delay))},_open:function(t){var i=e.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(i)},collapseAll:function(t,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:e(t&&t.target).closest(this.element.find(".ui-menu"));s.length||(s=this.element),this._close(s),this.blur(t),this.activeMenu=s},this.delay)},_close:function(e){e||(e=this.active?this.active.parent():this.element),e.find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false").end().find(".ui-state-active").not(".ui-state-focus").removeClass("ui-state-active")},_closeOnDocumentClick:function(t){return!e(t.target).closest(".ui-menu").length},_isDivider:function(e){return!/[^\-\u2014\u2013\s]/.test(e.text())},collapse:function(e){var t=this.active&&this.active.parent().closest(".ui-menu-item",this.element);t&&t.length&&(this._close(),this.focus(e,t))},expand:function(e){var t=this.active&&this.active.children(".ui-menu ").find(this.options.items).first();t&&t.length&&(this._open(t.parent()),this._delay(function(){this.focus(e,t)}))},next:function(e){this._move("next","first",e)},previous:function(e){this._move("prev","last",e)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_move:function(e,t,i){var s;this.active&&(s="first"===e||"last"===e?this.active["first"===e?"prevAll":"nextAll"](".ui-menu-item").eq(-1):this.active[e+"All"](".ui-menu-item").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[t]()),this.focus(i,s)},nextPage:function(t){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(".ui-menu-item").each(function(){return i=e(this),0>i.offset().top-s-n}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items)[this.active?"last":"first"]())),void 0):(this.next(t),void 0)},previousPage:function(t){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(".ui-menu-item").each(function(){return i=e(this),i.offset().top-s+n>0}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items).first())),void 0):(this.next(t),void 0)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||e(t.target).closest(".ui-menu-item");var i={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,i)},_filterMenuItems:function(t){var i=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),s=RegExp("^"+i,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return s.test(e.trim(e(this).text()))})}}),e.widget("ui.autocomplete",{version:"1.11.4",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var t,i,s,n=this.element[0].nodeName.toLowerCase(),a="textarea"===n,o="input"===n;this.isMultiLine=a?!0:o?!1:this.element.prop("isContentEditable"),this.valueMethod=this.element[a||o?"val":"text"],this.isNewMenu=!0,this.element.addClass("ui-autocomplete-input").attr("autocomplete","off"),this._on(this.element,{keydown:function(n){if(this.element.prop("readOnly"))return t=!0,s=!0,i=!0,void 0;t=!1,s=!1,i=!1;var a=e.ui.keyCode;switch(n.keyCode){case 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i=t.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==this.document[0].activeElement&&(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",e,{item:i})&&this._value(i.value),this.term=this._value(),this.close(e),this.selectedItem=i}}),this.liveRegion=e("<span>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).addClass("ui-helper-hidden-accessible").appendTo(this.document[0].body),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeClass("ui-autocomplete-input").removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(e,t){this._super(e,t),"source"===e&&this._initSource(),"appendTo"===e&&this.menu.element.appendTo(this._appendTo()),"disabled"===e&&t&&this.xhr&&this.xhr.abort()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_initSource:function(){var t,i,s=this;e.isArray(this.options.source)?(t=this.options.source,this.source=function(i,s){s(e.ui.autocomplete.filter(t,i.term))}):"string"==typeof this.options.source?(i=this.options.source,this.source=function(t,n){s.xhr&&s.xhr.abort(),s.xhr=e.ajax({url:i,data:t,dataType:"json",success:function(e){n(e)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(e){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),i=this.menu.element.is(":visible"),s=e.altKey||e.ctrlKey||e.metaKey||e.shiftKey;(!t||t&&!i&&!s)&&(this.selectedItem=null,this.search(null,e))},this.options.delay)},search:function(e,t){return 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e.proxy(function(e){t===this.requestIndex&&this.__response(e),this.pending--,this.pending||this.element.removeClass("ui-autocomplete-loading")},this)},__response:function(e){e&&(e=this._normalize(e)),this._trigger("response",null,{content:e}),!this.options.disabled&&e&&e.length&&!this.cancelSearch?(this._suggest(e),this._trigger("open")):this._close()},close:function(e){this.cancelSearch=!0,this._close(e)},_close:function(e){this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",e))},_change:function(e){this.previous!==this._value()&&this._trigger("change",e,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:e.map(t,function(t){return"string"==typeof t?{label:t,value:t}:e.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var i=this.menu.element.empty();this._renderMenu(i,t),this.isNewMenu=!0,this.menu.refresh(),i.show(),this._resizeMenu(),i.position(e.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next()},_resizeMenu:function(){var e=this.menu.element;e.outerWidth(Math.max(e.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(t,i){var s=this;e.each(i,function(e,i){s._renderItemData(t,i)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-autocomplete-item",t)},_renderItem:function(t,i){return e("<li>").text(i.label).appendTo(t)},_move:function(e,t){return this.menu.element.is(":visible")?this.menu.isFirstItem()&&/^previous/.test(e)||this.menu.isLastItem()&&/^next/.test(e)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[e](t),void 0):(this.search(null,t),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(e,t){(!this.isMultiLine||this.menu.element.is(":visible"))&&(this._move(e,t),t.preventDefault())}}),e.extend(e.ui.autocomplete,{escapeRegex:function(e){return e.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,i){var s=RegExp(e.ui.autocomplete.escapeRegex(i),"i");return e.grep(t,function(e){return s.test(e.label||e.value||e)})}}),e.widget("ui.autocomplete",e.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(e){return e+(e>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,this.liveRegion.children().hide(),e("<div>").text(i).appendTo(this.liveRegion))}}),e.ui.autocomplete;var r,h="ui-button ui-widget ui-state-default ui-corner-all",l="ui-button-icons-only ui-button-icon-only ui-button-text-icons ui-button-text-icon-primary ui-button-text-icon-secondary ui-button-text-only",u=function(){var t=e(this);setTimeout(function(){t.find(":ui-button").button("refresh")},1)},d=function(t){var i=t.name,s=t.form,n=e([]);return i&&(i=i.replace(/'/g,"\\'"),n=s?e(s).find("[name='"+i+"'][type=radio]"):e("[name='"+i+"'][type=radio]",t.ownerDocument).filter(function(){return!this.form})),n};e.widget("ui.button",{version:"1.11.4",defaultElement:"<button>",options:{disabled:null,text:!0,label:null,icons:{primary:null,secondary:null}},_create:function(){this.element.closest("form").unbind("reset"+this.eventNamespace).bind("reset"+this.eventNamespace,u),"boolean"!=typeof this.options.disabled?this.options.disabled=!!this.element.prop("disabled"):this.element.prop("disabled",this.options.disabled),this._determineButtonType(),this.hasTitle=!!this.buttonElement.attr("title");var t=this,i=this.options,s="checkbox"===this.type||"radio"===this.type,n=s?"":"ui-state-active";null===i.label&&(i.label="input"===this.type?this.buttonElement.val():this.buttonElement.html()),this._hoverable(this.buttonElement),this.buttonElement.addClass(h).attr("role","button").bind("mouseenter"+this.eventNamespace,function(){i.disabled||this===r&&e(this).addClass("ui-state-active")}).bind("mouseleave"+this.eventNamespace,function(){i.disabled||e(this).removeClass(n)}).bind("click"+this.eventNamespace,function(e){i.disabled&&(e.preventDefault(),e.stopImmediatePropagation())}),this._on({focus:function(){this.buttonElement.addClass("ui-state-focus")},blur:function(){this.buttonElement.removeClass("ui-state-focus")}}),s&&this.element.bind("change"+this.eventNamespace,function(){t.refresh()}),"checkbox"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){return i.disabled?!1:void 0}):"radio"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){if(i.disabled)return!1;e(this).addClass("ui-state-active"),t.buttonElement.attr("aria-pressed","true");var s=t.element[0];d(s).not(s).map(function(){return e(this).button("widget")[0]}).removeClass("ui-state-active").attr("aria-pressed","false")}):(this.buttonElement.bind("mousedown"+this.eventNamespace,function(){return i.disabled?!1:(e(this).addClass("ui-state-active"),r=this,t.document.one("mouseup",function(){r=null}),void 0)}).bind("mouseup"+this.eventNamespace,function(){return i.disabled?!1:(e(this).removeClass("ui-state-active"),void 0)}).bind("keydown"+this.eventNamespace,function(t){return i.disabled?!1:((t.keyCode===e.ui.keyCode.SPACE||t.keyCode===e.ui.keyCode.ENTER)&&e(this).addClass("ui-state-active"),void 0)}).bind("keyup"+this.eventNamespace+" blur"+this.eventNamespace,function(){e(this).removeClass("ui-state-active")}),this.buttonElement.is("a")&&this.buttonElement.keyup(function(t){t.keyCode===e.ui.keyCode.SPACE&&e(this).click()})),this._setOption("disabled",i.disabled),this._resetButton()},_determineButtonType:function(){var e,t,i;this.type=this.element.is("[type=checkbox]")?"checkbox":this.element.is("[type=radio]")?"radio":this.element.is("input")?"input":"button","checkbox"===this.type||"radio"===this.type?(e=this.element.parents().last(),t="label[for='"+this.element.attr("id")+"']",this.buttonElement=e.find(t),this.buttonElement.length||(e=e.length?e.siblings():this.element.siblings(),this.buttonElement=e.filter(t),this.buttonElement.length||(this.buttonElement=e.find(t))),this.element.addClass("ui-helper-hidden-accessible"),i=this.element.is(":checked"),i&&this.buttonElement.addClass("ui-state-active"),this.buttonElement.prop("aria-pressed",i)):this.buttonElement=this.element},widget:function(){return this.buttonElement},_destroy:function(){this.element.removeClass("ui-helper-hidden-accessible"),this.buttonElement.removeClass(h+" ui-state-active "+l).removeAttr("role").removeAttr("aria-pressed").html(this.buttonElement.find(".ui-button-text").html()),this.hasTitle||this.buttonElement.removeAttr("title")},_setOption:function(e,t){return this._super(e,t),"disabled"===e?(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),t&&("checkbox"===this.type||"radio"===this.type?this.buttonElement.removeClass("ui-state-focus"):this.buttonElement.removeClass("ui-state-focus ui-state-active")),void 0):(this._resetButton(),void 0)},refresh:function(){var t=this.element.is("input, button")?this.element.is(":disabled"):this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOption("disabled",t),"radio"===this.type?d(this.element[0]).each(function(){e(this).is(":checked")?e(this).button("widget").addClass("ui-state-active").attr("aria-pressed","true"):e(this).button("widget").removeClass("ui-state-active").attr("aria-pressed","false")}):"checkbox"===this.type&&(this.element.is(":checked")?this.buttonElement.addClass("ui-state-active").attr("aria-pressed","true"):this.buttonElement.removeClass("ui-state-active").attr("aria-pressed","false"))},_resetButton:function(){if("input"===this.type)return this.options.label&&this.element.val(this.options.label),void 0;var t=this.buttonElement.removeClass(l),i=e("<span></span>",this.document[0]).addClass("ui-button-text").html(this.options.label).appendTo(t.empty()).text(),s=this.options.icons,n=s.primary&&s.secondary,a=[];s.primary||s.secondary?(this.options.text&&a.push("ui-button-text-icon"+(n?"s":s.primary?"-primary":"-secondary")),s.primary&&t.prepend("<span class='ui-button-icon-primary ui-icon "+s.primary+"'></span>"),s.secondary&&t.append("<span class='ui-button-icon-secondary ui-icon "+s.secondary+"'></span>"),this.options.text||(a.push(n?"ui-button-icons-only":"ui-button-icon-only"),this.hasTitle||t.attr("title",e.trim(i)))):a.push("ui-button-text-only"),t.addClass(a.join(" "))}}),e.widget("ui.buttonset",{version:"1.11.4",options:{items:"button, input[type=button], input[type=submit], input[type=reset], input[type=checkbox], input[type=radio], a, :data(ui-button)"},_create:function(){this.element.addClass("ui-buttonset")},_init:function(){this.refresh()},_setOption:function(e,t){"disabled"===e&&this.buttons.button("option",e,t),this._super(e,t)},refresh:function(){var t="rtl"===this.element.css("direction"),i=this.element.find(this.options.items),s=i.filter(":ui-button");i.not(":ui-button").button(),s.button("refresh"),this.buttons=i.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-all ui-corner-left ui-corner-right").filter(":first").addClass(t?"ui-corner-right":"ui-corner-left").end().filter(":last").addClass(t?"ui-corner-left":"ui-corner-right").end().end()},_destroy:function(){this.element.removeClass("ui-buttonset"),this.buttons.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-left ui-corner-right").end().button("destroy")}}),e.ui.button,e.widget("ui.dialog",{version:"1.11.4",options:{appendTo:"body",autoOpen:!0,buttons:[],closeOnEscape:!0,closeText:"Close",dialogClass:"",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var i=e(this).css(t).offset().top;0>i&&e(this).css("top",t.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),this.options.title=this.options.title||this.originalTitle,this._createWrapper(),this.element.show().removeAttr("title").addClass("ui-dialog-content ui-widget-content").appendTo(this.uiDialog),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&e.fn.draggable&&this._makeDraggable(),this.options.resizable&&e.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?e(t):this.document.find(t||"body").eq(0)},_destroy:function(){var e,t=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().removeClass("ui-dialog-content ui-widget-content").css(this.originalCss).detach(),this.uiDialog.stop(!0,!0).remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),e=t.parent.children().eq(t.index),e.length&&e[0]!==this.element[0]?e.before(this.element):t.parent.append(this.element)},widget:function(){return this.uiDialog},disable:e.noop,enable:e.noop,close:function(t){var i,s=this;if(this._isOpen&&this._trigger("beforeClose",t)!==!1){if(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),!this.opener.filter(":focusable").focus().length)try{i=this.document[0].activeElement,i&&"body"!==i.nodeName.toLowerCase()&&e(i).blur()}catch(n){}this._hide(this.uiDialog,this.options.hide,function(){s._trigger("close",t)})}},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,i){var s=!1,n=this.uiDialog.siblings(".ui-front:visible").map(function(){return+e(this).css("z-index")}).get(),a=Math.max.apply(null,n);return a>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",a+1),s=!0),s&&!i&&this._trigger("focus",t),s},open:function(){var t=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 0):(this._isOpen=!0,this.opener=e(this.document[0].activeElement),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"),void 0)},_focusTabbable:function(){var e=this._focusedElement;e||(e=this.element.find("[autofocus]")),e.length||(e=this.element.find(":tabbable")),e.length||(e=this.uiDialogButtonPane.find(":tabbable")),e.length||(e=this.uiDialogTitlebarClose.filter(":tabbable")),e.length||(e=this.uiDialog),e.eq(0).focus()},_keepFocus:function(t){function i(){var t=this.document[0].activeElement,i=this.uiDialog[0]===t||e.contains(this.uiDialog[0],t);i||this._focusTabbable()}t.preventDefault(),i.call(this),this._delay(i)},_createWrapper:function(){this.uiDialog=e("<div>").addClass("ui-dialog ui-widget ui-widget-content ui-corner-all ui-front "+this.options.dialogClass).hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===e.ui.keyCode.ESCAPE)return t.preventDefault(),this.close(t),void 0;if(t.keyCode===e.ui.keyCode.TAB&&!t.isDefaultPrevented()){var i=this.uiDialog.find(":tabbable"),s=i.filter(":first"),n=i.filter(":last");t.target!==n[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==s[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){n.focus()}),t.preventDefault()):(this._delay(function(){s.focus()}),t.preventDefault())}},mousedown:function(e){this._moveToTop(e)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=e("<div>").addClass("ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix").prependTo(this.uiDialog),this._on(this.uiDialogTitlebar,{mousedown:function(t){e(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.focus()}}),this.uiDialogTitlebarClose=e("<button type='button'></button>").button({label:this.options.closeText,icons:{primary:"ui-icon-closethick"},text:!1}).addClass("ui-dialog-titlebar-close").appendTo(this.uiDialogTitlebar),this._on(this.uiDialogTitlebarClose,{click:function(e){e.preventDefault(),this.close(e)}}),t=e("<span>").uniqueId().addClass("ui-dialog-title").prependTo(this.uiDialogTitlebar),this._title(t),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(e){this.options.title||e.html("&#160;"),e.text(this.options.title)
},_createButtonPane:function(){this.uiDialogButtonPane=e("<div>").addClass("ui-dialog-buttonpane ui-widget-content ui-helper-clearfix"),this.uiButtonSet=e("<div>").addClass("ui-dialog-buttonset").appendTo(this.uiDialogButtonPane),this._createButtons()},_createButtons:function(){var t=this,i=this.options.buttons;return this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),e.isEmptyObject(i)||e.isArray(i)&&!i.length?(this.uiDialog.removeClass("ui-dialog-buttons"),void 0):(e.each(i,function(i,s){var n,a;s=e.isFunction(s)?{click:s,text:i}:s,s=e.extend({type:"button"},s),n=s.click,s.click=function(){n.apply(t.element[0],arguments)},a={icons:s.icons,text:s.showText},delete s.icons,delete s.showText,e("<button></button>",s).button(a).appendTo(t.uiButtonSet)}),this.uiDialog.addClass("ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog),void 0)},_makeDraggable:function(){function t(e){return{position:e.position,offset:e.offset}}var i=this,s=this.options;this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(s,n){e(this).addClass("ui-dialog-dragging"),i._blockFrames(),i._trigger("dragStart",s,t(n))},drag:function(e,s){i._trigger("drag",e,t(s))},stop:function(n,a){var o=a.offset.left-i.document.scrollLeft(),r=a.offset.top-i.document.scrollTop();s.position={my:"left top",at:"left"+(o>=0?"+":"")+o+" "+"top"+(r>=0?"+":"")+r,of:i.window},e(this).removeClass("ui-dialog-dragging"),i._unblockFrames(),i._trigger("dragStop",n,t(a))}})},_makeResizable:function(){function t(e){return{originalPosition:e.originalPosition,originalSize:e.originalSize,position:e.position,size:e.size}}var i=this,s=this.options,n=s.resizable,a=this.uiDialog.css("position"),o="string"==typeof n?n:"n,e,s,w,se,sw,ne,nw";this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:s.maxWidth,maxHeight:s.maxHeight,minWidth:s.minWidth,minHeight:this._minHeight(),handles:o,start:function(s,n){e(this).addClass("ui-dialog-resizing"),i._blockFrames(),i._trigger("resizeStart",s,t(n))},resize:function(e,s){i._trigger("resize",e,t(s))},stop:function(n,a){var o=i.uiDialog.offset(),r=o.left-i.document.scrollLeft(),h=o.top-i.document.scrollTop();s.height=i.uiDialog.height(),s.width=i.uiDialog.width(),s.position={my:"left top",at:"left"+(r>=0?"+":"")+r+" "+"top"+(h>=0?"+":"")+h,of:i.window},e(this).removeClass("ui-dialog-resizing"),i._unblockFrames(),i._trigger("resizeStop",n,t(a))}}).css("position",a)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=e(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),i=e.inArray(this,t);-1!==i&&t.splice(i,1)},_trackingInstances:function(){var e=this.document.data("ui-dialog-instances");return e||(e=[],this.document.data("ui-dialog-instances",e)),e},_minHeight:function(){var e=this.options;return"auto"===e.height?e.minHeight:Math.min(e.minHeight,e.height)},_position:function(){var e=this.uiDialog.is(":visible");e||this.uiDialog.show(),this.uiDialog.position(this.options.position),e||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};e.each(t,function(e,t){i._setOption(e,t),e in i.sizeRelatedOptions&&(s=!0),e in i.resizableRelatedOptions&&(n[e]=t)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(e,t){var i,s,n=this.uiDialog;"dialogClass"===e&&n.removeClass(this.options.dialogClass).addClass(t),"disabled"!==e&&(this._super(e,t),"appendTo"===e&&this.uiDialog.appendTo(this._appendTo()),"buttons"===e&&this._createButtons(),"closeText"===e&&this.uiDialogTitlebarClose.button({label:""+t}),"draggable"===e&&(i=n.is(":data(ui-draggable)"),i&&!t&&n.draggable("destroy"),!i&&t&&this._makeDraggable()),"position"===e&&this._position(),"resizable"===e&&(s=n.is(":data(ui-resizable)"),s&&!t&&n.resizable("destroy"),s&&"string"==typeof t&&n.resizable("option","handles",t),s||t===!1||this._makeResizable()),"title"===e&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var e,t,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),e=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),t=Math.max(0,s.minHeight-e),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-e):"none","auto"===s.height?this.element.css({minHeight:t,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-e)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=e(this);return e("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return e(t.target).closest(".ui-dialog").length?!0:!!e(t.target).closest(".ui-datepicker").length},_createOverlay:function(){if(this.options.modal){var t=!0;this._delay(function(){t=!1}),this.document.data("ui-dialog-overlays")||this._on(this.document,{focusin:function(e){t||this._allowInteraction(e)||(e.preventDefault(),this._trackingInstances()[0]._focusTabbable())}}),this.overlay=e("<div>").addClass("ui-widget-overlay ui-front").appendTo(this._appendTo()),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1)}},_destroyOverlay:function(){if(this.options.modal&&this.overlay){var e=this.document.data("ui-dialog-overlays")-1;e?this.document.data("ui-dialog-overlays",e):this.document.unbind("focusin").removeData("ui-dialog-overlays"),this.overlay.remove(),this.overlay=null}}}),e.widget("ui.progressbar",{version:"1.11.4",options:{max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.addClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").attr({role:"progressbar","aria-valuemin":this.min}),this.valueDiv=e("<div class='ui-progressbar-value ui-widget-header ui-corner-left'></div>").appendTo(this.element),this._refreshValue()},_destroy:function(){this.element.removeClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.valueDiv.remove()},value:function(e){return void 0===e?this.options.value:(this.options.value=this._constrainedValue(e),this._refreshValue(),void 0)},_constrainedValue:function(e){return void 0===e&&(e=this.options.value),this.indeterminate=e===!1,"number"!=typeof e&&(e=0),this.indeterminate?!1:Math.min(this.options.max,Math.max(this.min,e))},_setOptions:function(e){var t=e.value;delete e.value,this._super(e),this.options.value=this._constrainedValue(t),this._refreshValue()},_setOption:function(e,t){"max"===e&&(t=Math.max(this.min,t)),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,i=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).toggleClass("ui-corner-right",t===this.options.max).width(i.toFixed(0)+"%"),this.element.toggleClass("ui-progressbar-indeterminate",this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=e("<div class='ui-progressbar-overlay'></div>").appendTo(this.valueDiv))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),e.widget("ui.selectmenu",{version:"1.11.4",defaultElement:"<select>",options:{appendTo:null,disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:null,change:null,close:null,focus:null,open:null,select:null},_create:function(){var e=this.element.uniqueId().attr("id");this.ids={element:e,button:e+"-button",menu:e+"-menu"},this._drawButton(),this._drawMenu(),this.options.disabled&&this.disable()},_drawButton:function(){var t=this;this.label=e("label[for='"+this.ids.element+"']").attr("for",this.ids.button),this._on(this.label,{click:function(e){this.button.focus(),e.preventDefault()}}),this.element.hide(),this.button=e("<span>",{"class":"ui-selectmenu-button ui-widget ui-state-default ui-corner-all",tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true"}).insertAfter(this.element),e("<span>",{"class":"ui-icon "+this.options.icons.button}).prependTo(this.button),this.buttonText=e("<span>",{"class":"ui-selectmenu-text"}).appendTo(this.button),this._setText(this.buttonText,this.element.find("option:selected").text()),this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){t.menuItems||t._refreshMenu()}),this._hoverable(this.button),this._focusable(this.button)},_drawMenu:function(){var t=this;this.menu=e("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=e("<div>",{"class":"ui-selectmenu-menu ui-front"}).append(this.menu).appendTo(this._appendTo()),this.menuInstance=this.menu.menu({role:"listbox",select:function(e,i){e.preventDefault(),t._setSelection(),t._select(i.item.data("ui-selectmenu-item"),e)},focus:function(e,i){var s=i.item.data("ui-selectmenu-item");null!=t.focusIndex&&s.index!==t.focusIndex&&(t._trigger("focus",e,{item:s}),t.isOpen||t._select(s,e)),t.focusIndex=s.index,t.button.attr("aria-activedescendant",t.menuItems.eq(s.index).attr("id"))}}).menu("instance"),this.menu.addClass("ui-corner-bottom").removeClass("ui-corner-all"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this._setText(this.buttonText,this._getSelectedItem().text()),this.options.width||this._resizeButton()},_refreshMenu:function(){this.menu.empty();var e,t=this.element.find("option");t.length&&(this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup"),e=this._getSelectedItem(),this.menuInstance.focus(null,e),this._setAria(e.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(e){this.options.disabled||(this.menuItems?(this.menu.find(".ui-state-focus").removeClass("ui-state-focus"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",e))},_position:function(){this.menuWrap.position(e.extend({of:this.button},this.options.position))},close:function(e){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",e))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderMenu:function(t,i){var s=this,n="";e.each(i,function(i,a){a.optgroup!==n&&(e("<li>",{"class":"ui-selectmenu-optgroup ui-menu-divider"+(a.element.parent("optgroup").prop("disabled")?" ui-state-disabled":""),text:a.optgroup}).appendTo(t),n=a.optgroup),s._renderItemData(t,a)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-selectmenu-item",t)},_renderItem:function(t,i){var s=e("<li>");return i.disabled&&s.addClass("ui-state-disabled"),this._setText(s,i.label),s.appendTo(t)},_setText:function(e,t){t?e.text(t):e.html("&#160;")},_move:function(e,t){var i,s,n=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex):(i=this.menuItems.eq(this.element[0].selectedIndex),n+=":not(.ui-state-disabled)"),s="first"===e||"last"===e?i["first"===e?"prevAll":"nextAll"](n).eq(-1):i[e+"All"](n).eq(0),s.length&&this.menuInstance.focus(t,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex)},_toggle:function(e){this[this.isOpen?"close":"open"](e)},_setSelection:function(){var e;this.range&&(window.getSelection?(e=window.getSelection(),e.removeAllRanges(),e.addRange(this.range)):this.range.select(),this.button.focus())},_documentClick:{mousedown:function(t){this.isOpen&&(e(t.target).closest(".ui-selectmenu-menu, #"+this.ids.button).length||this.close(t))}},_buttonEvents:{mousedown:function(){var e;window.getSelection?(e=window.getSelection(),e.rangeCount&&(this.range=e.getRangeAt(0))):this.range=document.selection.createRange()},click:function(e){this._setSelection(),this._toggle(e)},keydown:function(t){var i=!0;switch(t.keyCode){case e.ui.keyCode.TAB:case e.ui.keyCode.ESCAPE:this.close(t),i=!1;break;case e.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case e.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case e.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case e.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case e.ui.keyCode.LEFT:this._move("prev",t);break;case e.ui.keyCode.RIGHT:this._move("next",t);break;case e.ui.keyCode.HOME:case e.ui.keyCode.PAGE_UP:this._move("first",t);break;case e.ui.keyCode.END:case e.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),i=!1}i&&t.preventDefault()}},_selectFocusedItem:function(e){var t=this.menuItems.eq(this.focusIndex);t.hasClass("ui-state-disabled")||this._select(t.data("ui-selectmenu-item"),e)},_select:function(e,t){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=e.index,this._setText(this.buttonText,e.label),this._setAria(e),this._trigger("select",t,{item:e}),e.index!==i&&this._trigger("change",t,{item:e}),this.close(t)},_setAria:function(e){var t=this.menuItems.eq(e.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(e,t){"icons"===e&&this.button.find("span.ui-icon").removeClass(this.options.icons.button).addClass(t.button),this._super(e,t),"appendTo"===e&&this.menuWrap.appendTo(this._appendTo()),"disabled"===e&&(this.menuInstance.option("disabled",t),this.button.toggleClass("ui-state-disabled",t).attr("aria-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)),"width"===e&&this._resizeButton()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_toggleAttr:function(){this.button.toggleClass("ui-corner-top",this.isOpen).toggleClass("ui-corner-all",!this.isOpen).attr("aria-expanded",this.isOpen),this.menuWrap.toggleClass("ui-selectmenu-open",this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var e=this.options.width;e||(e=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(e)},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){return{disabled:this.element.prop("disabled")}},_parseOptions:function(t){var i=[];t.each(function(t,s){var 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t=this.options,i="";t.range?(t.range===!0&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:e.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?this.range.removeClass("ui-slider-range-min ui-slider-range-max").css({left:"",bottom:""}):(this.range=e("<div></div>").appendTo(this.element),i="ui-slider-range ui-widget-header ui-corner-all"),this.range.addClass(i+("min"===t.range||"max"===t.range?" ui-slider-range-"+t.range:""))):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this.element.removeClass("ui-slider ui-slider-horizontal ui-slider-vertical ui-widget ui-widget-content ui-corner-all"),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,a,o,r,h,l,u=this,d=this.options;return d.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:t.pageX,y:t.pageY},s=this._normValueFromMouse(i),n=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var i=Math.abs(s-u.values(t));(n>i||n===i&&(t===u._lastChangedValue||u.values(t)===d.min))&&(n=i,a=e(this),o=t)}),r=this._start(t,o),r===!1?!1:(this._mouseSliding=!0,this._handleIndex=o,a.addClass("ui-state-active").focus(),h=a.offset(),l=!e(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=l?{left:0,top:0}:{left:t.pageX-h.left-a.width()/2,top:t.pageY-h.top-a.height()/2-(parseInt(a.css("borderTopWidth"),10)||0)-(parseInt(a.css("borderBottomWidth"),10)||0)+(parseInt(a.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,s),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(e){var t={x:e.pageX,y:e.pageY},i=this._normValueFromMouse(t);return this._slide(e,this._handleIndex,i),!1},_mouseStop:function(e){return this.handles.removeClass("ui-state-active"),this._mouseSliding=!1,this._stop(e,this._handleIndex),this._change(e,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(e){var t,i,s,n,a;return"horizontal"===this.orientation?(t=this.elementSize.width,i=e.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(t=this.elementSize.height,i=e.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),s=i/t,s>1&&(s=1),0>s&&(s=0),"vertical"===this.orientation&&(s=1-s),n=this._valueMax()-this._valueMin(),a=this._valueMin()+s*n,this._trimAlignValue(a)},_start:function(e,t){var i={handle:this.handles[t],value:this.value()};return this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("start",e,i)},_slide:function(e,t,i){var s,n,a;this.options.values&&this.options.values.length?(s=this.values(t?0:1),2===this.options.values.length&&this.options.range===!0&&(0===t&&i>s||1===t&&s>i)&&(i=s),i!==this.values(t)&&(n=this.values(),n[t]=i,a=this._trigger("slide",e,{handle:this.handles[t],value:i,values:n}),s=this.values(t?0:1),a!==!1&&this.values(t,i))):i!==this.value()&&(a=this._trigger("slide",e,{handle:this.handles[t],value:i}),a!==!1&&this.value(i))},_stop:function(e,t){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("stop",e,i)},_change:function(e,t){if(!this._keySliding&&!this._mouseSliding){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._lastChangedValue=t,this._trigger("change",e,i)}},value:function(e){return arguments.length?(this.options.value=this._trimAlignValue(e),this._refreshValue(),this._change(null,0),void 0):this._value()},values:function(t,i){var s,n,a;if(arguments.length>1)return this.options.values[t]=this._trimAlignValue(i),this._refreshValue(),this._change(null,t),void 0;if(!arguments.length)return this._values();if(!e.isArray(arguments[0]))return this.options.values&&this.options.values.length?this._values(t):this.value();for(s=this.options.values,n=arguments[0],a=0;s.length>a;a+=1)s[a]=this._trimAlignValue(n[a]),this._change(null,a);this._refreshValue()},_setOption:function(t,i){var s,n=0;switch("range"===t&&this.options.range===!0&&("min"===i?(this.options.value=this._values(0),this.options.values=null):"max"===i&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),e.isArray(this.options.values)&&(n=this.options.values.length),"disabled"===t&&this.element.toggleClass("ui-state-disabled",!!i),this._super(t,i),t){case"orientation":this._detectOrientation(),this.element.removeClass("ui-slider-horizontal 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this._valueMax();var t=this.options.step>0?this.options.step:1,i=(e-this._valueMin())%t,s=e-i;return 2*Math.abs(i)>=t&&(s+=i>0?t:-t),parseFloat(s.toFixed(5))},_calculateNewMax:function(){var e=this.options.max,t=this._valueMin(),i=this.options.step,s=Math.floor(+(e-t).toFixed(this._precision())/i)*i;e=s+t,this.max=parseFloat(e.toFixed(this._precision()))},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshValue:function(){var t,i,s,n,a,o=this.options.range,r=this.options,h=this,l=this._animateOff?!1:r.animate,u={};this.options.values&&this.options.values.length?this.handles.each(function(s){i=100*((h.values(s)-h._valueMin())/(h._valueMax()-h._valueMin())),u["horizontal"===h.orientation?"left":"bottom"]=i+"%",e(this).stop(1,1)[l?"animate":"css"](u,r.animate),h.options.range===!0&&("horizontal"===h.orientation?(0===s&&h.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({width:i-t+"%"},{queue:!1,duration:r.animate})):(0===s&&h.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({height:i-t+"%"},{queue:!1,duration:r.animate}))),t=i}):(s=this.value(),n=this._valueMin(),a=this._valueMax(),i=a!==n?100*((s-n)/(a-n)):0,u["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](u,r.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},r.animate),"max"===o&&"horizontal"===this.orientation&&this.range[l?"animate":"css"]({width:100-i+"%"},{queue:!1,duration:r.animate}),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},r.animate),"max"===o&&"vertical"===this.orientation&&this.range[l?"animate":"css"]({height:100-i+"%"},{queue:!1,duration:r.animate}))},_handleEvents:{keydown:function(t){var 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-<script src="data:application/x-javascript;base64,/* jquery Tocify - v1.9.1 - 2013-10-22
 * http://www.gregfranko.com/jquery.tocify.js/
 * Copyright (c) 2013 Greg Franko; Licensed MIT */

// Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic.  I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE.
(function(tocify) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    // Calls the second IIFE and locally passes in the global jQuery, window, and document objects
    tocify(window.jQuery, window, document);

  }

  // Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable.  The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set.  All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript.
  (function($, window, document, undefined) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    var tocClassName = "tocify",
      tocClass = "." + tocClassName,
      tocFocusClassName = "tocify-focus",
      tocHoverClassName = "tocify-hover",
      hideTocClassName = "tocify-hide",
      hideTocClass = "." + hideTocClassName,
      headerClassName = "tocify-header",
      headerClass = "." + headerClassName,
      subheaderClassName = "tocify-subheader",
      subheaderClass = "." + subheaderClassName,
      itemClassName = "tocify-item",
      itemClass = "." + itemClassName,
      extendPageClassName = "tocify-extend-page",
      extendPageClass = "." + extendPageClassName;

    // Calling the jQueryUI Widget Factory Method
    $.widget("toc.tocify", {

      //Plugin version
      version: "1.9.1",

      // These options will be used as defaults
      options: {

        // **context**: Accepts String: Any jQuery selector
        // The container element that holds all of the elements used to generate the table of contents
        context: "body",

        // **ignoreSelector**: Accepts String: Any jQuery selector
        // A selector to any element that would be matched by selectors that you wish to be ignored
        ignoreSelector: null,

        // **selectors**: Accepts an Array of Strings: Any jQuery selectors
        // The element's used to generate the table of contents.  The order is very important since it will determine the table of content's nesting structure
        selectors: "h1, h2, h3",

        // **showAndHide**: Accepts a boolean: true or false
        // Used to determine if elements should be shown and hidden
        showAndHide: true,

        // **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown"
        // Used to display any of the table of contents nested items
        showEffect: "slideDown",

        // **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the show animation
        showEffectSpeed: "medium",

        // **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp"
        // Used to hide any of the table of contents nested items
        hideEffect: "slideUp",

        // **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the hide animation
        hideEffectSpeed: "medium",

        // **smoothScroll**: Accepts a boolean: true or false
        // Determines if a jQuery animation should be used to scroll to specific table of contents items on the page
        smoothScroll: true,

        // **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the smoothScroll animation
        smoothScrollSpeed: "medium",

        // **scrollTo**: Accepts Number (pixels)
        // The amount of space between the top of page and the selected table of contents item after the page has been scrolled
        scrollTo: 0,

        // **showAndHideOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be shown and hidden while scrolling
        showAndHideOnScroll: true,

        // **highlightOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be highlighted (set to a different color) while scrolling
        highlightOnScroll: true,

        // **highlightOffset**: Accepts a number
        // The offset distance in pixels to trigger the next active table of contents item
        highlightOffset: 40,

        // **theme**: Accepts a string: "bootstrap", "jqueryui", or "none"
        // Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents
        theme: "bootstrap",

        // **extendPage**: Accepts a boolean: true or false
        // If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased
        extendPage: true,

        // **extendPageOffset**: Accepts a number: pixels
        // How close to the bottom of the page a user must scroll before the page is extended
        extendPageOffset: 100,

        // **history**: Accepts a boolean: true or false
        // Adds a hash to the page url to maintain history
        history: true,

        // **scrollHistory**: Accepts a boolean: true or false
        // Adds a hash to the page url, to maintain history, when scrolling to a TOC item
        scrollHistory: false,

        // **hashGenerator**: How the hash value (the anchor segment of the URL, following the
        // # character) will be generated.
        //
        // "compact" (default) - #CompressesEverythingTogether
        // "pretty" - #looks-like-a-nice-url-and-is-easily-readable
        // function(text, element){} - Your own hash generation function that accepts the text as an
        // argument, and returns the hash value.
        hashGenerator: "compact",

        // **highlightDefault**: Accepts a boolean: true or false
        // Set's the first TOC item as active if no other TOC item is active.
        highlightDefault: true

      },

      // _Create
      // -------
      //      Constructs the plugin.  Only called once.
      _create: function() {

        var self = this;

        self.extendPageScroll = true;

        // Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings)
        self.items = [];

        // Generates the HTML for the dynamic table of contents
        self._generateToc();

        // Adds CSS classes to the newly generated table of contents HTML
        self._addCSSClasses();

        self.webkit = (function() {

          for (var prop in window) {

            if (prop) {

              if (prop.toLowerCase().indexOf("webkit") !== -1) {

                return true;

              }

            }

          }

          return false;

        }());

        // Adds jQuery event handlers to the newly generated table of contents
        self._setEventHandlers();

        // Binding to the Window load event to make sure the correct scrollTop is calculated
        $(window).load(function() {

          // Sets the active TOC item
          self._setActiveElement(true);

          // Once all animations on the page are complete, this callback function will be called
          $("html, body").promise().done(function() {

            setTimeout(function() {

              self.extendPageScroll = false;

            }, 0);

          });

        });

      },

      // _generateToc
      // ------------
      //      Generates the HTML for the dynamic table of contents
      _generateToc: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above
          firstElem,

          // Instantiated variable that will store the top level newly created unordered list DOM element
          ul,
          ignoreSelector = self.options.ignoreSelector;


        // Determine the element to start the toc with
        // get all the top level selectors
        firstElem = [];
        var selectors = this.options.selectors.replace(/ /g, "").split(",");
        // find the first set that have at least one non-ignored element
        for(var i = 0; i < selectors.length; i++) {
          var foundSelectors = $(this.options.context).find(selectors[i]);
          for (var s = 0; s < foundSelectors.length; s++) {
            if (!$(foundSelectors[s]).is(ignoreSelector)) {
              firstElem = foundSelectors;
              break;
            }
          }
          if (firstElem.length> 0)
            break;
        }

        if (!firstElem.length) {

          self.element.addClass(hideTocClassName);

          return;

        }

        self.element.addClass(tocClassName);

        // Loops through each top level selector
        firstElem.each(function(index) {

          //If the element matches the ignoreSelector then we skip it
          if ($(this).is(ignoreSelector)) {
            return;
          }

          // Creates an unordered list HTML element and adds a dynamic ID and standard class name
          ul = $("<ul/>", {
            "id": headerClassName + index,
            "class": headerClassName
          }).

          // Appends a top level list item HTML element to the previously created HTML header
          append(self._nestElements($(this), index));

          // Add the created unordered list element to the HTML element calling the plugin
          self.element.append(ul);

          // Finds all of the HTML tags between the header and subheader elements
          $(this).nextUntil(this.nodeName.toLowerCase()).each(function() {

            // If there are no nested subheader elemements
            if ($(this).find(self.options.selectors).length === 0) {

              // Loops through all of the subheader elements
              $(this).filter(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

            // If there are nested subheader elements
            else {

              // Loops through all of the subheader elements
              $(this).find(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

          });

        });

      },

      _setActiveElement: function(pageload) {

        var self = this,

          hash = window.location.hash.substring(1),

          elem = self.element.find('li[data-unique="' + hash + '"]');

        if (hash.length) {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          elem.addClass(self.focusClass);

          // Triggers the click event on the currently focused TOC item
          elem.click();

        } else {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          if (!hash.length && pageload && self.options.highlightDefault) {

            // Highlights the first TOC item if no other items are highlighted
            self.element.find(itemClass).first().addClass(self.focusClass);

          }

        }

        return self;

      },

      // _nestElements
      // -------------
      //      Helps create the table of contents list by appending nested list items
      _nestElements: function(self, index) {

        var arr, item, hashValue;

        arr = $.grep(this.items, function(item) {

          return item === self.text();

        });

        // If there is already a duplicate TOC item
        if (arr.length) {

          // Adds the current TOC item text and index (for slight randomization) to the internal array
          this.items.push(self.text() + index);

        }

        // If there not a duplicate TOC item
        else {

          // Adds the current TOC item text to the internal array
          this.items.push(self.text());

        }

        hashValue = this._generateHashValue(arr, self, index);

        // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
        item = $("<li/>", {

          // Sets a common class name to the list item
          "class": itemClassName,

          "data-unique": hashValue

        });

        if (this.options.theme !== "bootstrap3") {

          item.append($("<a/>", {

            "text": self.text()

          }));

        } else {

          item.text(self.text());

        }

        // Adds an HTML anchor tag before the currently traversed HTML element
        self.before($("<div/>", {

          // Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore)
          "name": hashValue,

          "data-unique": hashValue

        }));

        return item;

      },

      // _generateHashValue
      // ------------------
      //      Generates the hash value that will be used to refer to each item.
      _generateHashValue: function(arr, self, index) {

        var hashValue = "",
          hashGeneratorOption = this.options.hashGenerator;

        if (hashGeneratorOption === "pretty") {

          // prettify the text
          hashValue = self.text().toLowerCase().replace(/\s/g, "-");

          // fix double hyphens
          while (hashValue.indexOf("--") > -1) {
            hashValue = hashValue.replace(/--/g, "-");
          }

          // fix colon-space instances
          while (hashValue.indexOf(":-") > -1) {
            hashValue = hashValue.replace(/:-/g, "-");
          }

        } else if (typeof hashGeneratorOption === "function") {

          // call the function
          hashValue = hashGeneratorOption(self.text(), self);

        } else {

          // compact - the default
          hashValue = self.text().replace(/\s/g, "");

        }

        // add the index if we need to
        if (arr.length) {
          hashValue += "" + index;
        }

        // return the value
        return hashValue;

      },

      // _appendElements
      // ---------------
      //      Helps create the table of contents list by appending subheader elements

      _appendSubheaders: function(self, ul) {

        // The current element index
        var index = $(this).index(self.options.selectors),

          // Finds the previous header DOM element
          previousHeader = $(self.options.selectors).eq(index - 1),

          currentTagName = +$(this).prop("tagName").charAt(1),

          previousTagName = +previousHeader.prop("tagName").charAt(1),

          lastSubheader;

        // If the current header DOM element is smaller than the previous header DOM element or the first subheader
        if (currentTagName < previousTagName) {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index));

        }

        // If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element
        else if (currentTagName === previousTagName) {

          ul.find(itemClass).last().after(self._nestElements($(this), index));

        } else {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          ul.find(itemClass).last().

          // Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name
          after($("<ul/>", {

            "class": subheaderClassName,

            "data-tag": currentTagName

          })).next(subheaderClass).

          // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
          append(self._nestElements($(this), index));
        }

      },

      // _setEventHandlers
      // ----------------
      //      Adds jQuery event handlers to the newly generated table of contents
      _setEventHandlers: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // Instantiates a new variable that will be used to hold a specific element's context
          $self,

          // Instantiates a new variable that will be used to determine the smoothScroll animation time duration
          duration;

        // Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin
        this.element.on("click.tocify", "li", function(event) {

          if (self.options.history) {

            window.location.hash = $(this).attr("data-unique");

          }

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          $(this).addClass(self.focusClass);

          // If the showAndHide option is true
          if (self.options.showAndHide) {

            var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]');

            self._triggerShow(elem);

          }

          self._scrollTo($(this));

        });

        // Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin
        this.element.find("li").on({

          // Mouseenter event handler
          "mouseenter.tocify": function() {

            // Adds a hover CSS class to the current list item
            $(this).addClass(self.hoverClass);

            // Makes sure the cursor is set to the pointer icon
            $(this).css("cursor", "pointer");

          },

          // Mouseleave event handler
          "mouseleave.tocify": function() {

            if (self.options.theme !== "bootstrap") {

              // Removes the hover CSS class from the current list item
              $(this).removeClass(self.hoverClass);

            }

          }
        });

        // only attach handler if needed (expensive in IE)
        if (self.options.extendPage || self.options.highlightOnScroll || self.options.scrollHistory || self.options.showAndHideOnScroll) {
          // Window scroll event handler
          $(window).on("scroll.tocify", function() {

            // Once all animations on the page are complete, this callback function will be called
            $("html, body").promise().done(function() {

              // Local variables

              // Stores how far the user has scrolled
              var winScrollTop = $(window).scrollTop(),

                // Stores the height of the window
                winHeight = $(window).height(),

                // Stores the height of the document
                docHeight = $(document).height(),

                scrollHeight = $("body")[0].scrollHeight,

                // Instantiates a variable that will be used to hold a selected HTML element
                elem,

                lastElem,

                lastElemOffset,

                currentElem;

              if (self.options.extendPage) {

                // If the user has scrolled to the bottom of the page and the last toc item is not focused
                if ((self.webkit && winScrollTop >= scrollHeight - winHeight - self.options.extendPageOffset) || (!self.webkit && winHeight + winScrollTop > docHeight - self.options.extendPageOffset)) {

                  if (!$(extendPageClass).length) {

                    lastElem = $('div[data-unique="' + $(itemClass).last().attr("data-unique") + '"]');

                    if (!lastElem.length) return;

                    // Gets the top offset of the page header that is linked to the last toc item
                    lastElemOffset = lastElem.offset().top;

                    // Appends a div to the bottom of the page and sets the height to the difference of the window scrollTop and the last element's position top offset
                    $(self.options.context).append($("<div/>", {

                      "class": extendPageClassName,

                      "height": Math.abs(lastElemOffset - winScrollTop) + "px",

                      "data-unique": extendPageClassName

                    }));

                    if (self.extendPageScroll) {

                      currentElem = self.element.find('li.' + self.focusClass);

                      self._scrollTo($('div[data-unique="' + currentElem.attr("data-unique") + '"]'));

                    }

                  }

                }

              }

              // The zero timeout ensures the following code is run after the scroll events
              setTimeout(function() {

                // _Local variables_

                // Stores the distance to the closest anchor
                var closestAnchorDistance = null,

                  // Stores the index of the closest anchor
                  closestAnchorIdx = null,

                  // Keeps a reference to all anchors
                  anchors = $(self.options.context).find("div[data-unique]"),

                  anchorText;

                // Determines the index of the closest anchor
                anchors.each(function(idx) {
                  var distance = Math.abs(($(this).next().length ? $(this).next() : $(this)).offset().top - winScrollTop - self.options.highlightOffset);
                  if (closestAnchorDistance == null || distance < closestAnchorDistance) {
                    closestAnchorDistance = distance;
                    closestAnchorIdx = idx;
                  } else {
                    return false;
                  }
                });

                anchorText = $(anchors[closestAnchorIdx]).attr("data-unique");

                // Stores the list item HTML element that corresponds to the currently traversed anchor tag
                elem = $('li[data-unique="' + anchorText + '"]');

                // If the `highlightOnScroll` option is true and a next element is found
                if (self.options.highlightOnScroll && elem.length) {

                  // Removes highlighting from all of the list item's
                  self.element.find("." + self.focusClass).removeClass(self.focusClass);

                  // Highlights the corresponding list item
                  elem.addClass(self.focusClass);

                }

                if (self.options.scrollHistory) {

                  if (window.location.hash !== "#" + anchorText) {

                    window.location.replace("#" + anchorText);

                  }
                }

                // If the `showAndHideOnScroll` option is true
                if (self.options.showAndHideOnScroll && self.options.showAndHide) {

                  self._triggerShow(elem, true);

                }

              }, 0);

            });

          });
        }

      },

      // Show
      // ----
      //      Opens the current sub-header
      show: function(elem, scroll) {

        // Stores the plugin context in the `self` variable
        var self = this,
          element = elem;

        // If the sub-header is not already visible
        if (!elem.is(":visible")) {

          // If the current element does not have any nested subheaders, is not a header, and its parent is not visible
          if (!elem.find(subheaderClass).length && !elem.parent().is(headerClass) && !elem.parent().is(":visible")) {

            // Sets the current element to all of the subheaders within the current header
            elem = elem.parents(subheaderClass).add(elem);

          }

          // If the current element does not have any nested subheaders and is not a header
          else if (!elem.children(subheaderClass).length && !elem.parent().is(headerClass)) {

            // Sets the current element to the closest subheader
            elem = elem.closest(subheaderClass);

          }

          //Determines what jQuery effect to use
          switch (self.options.showEffect) {

            //Uses `no effect`
            case "none":

              elem.show();

              break;

              //Uses the jQuery `show` special effect
            case "show":

              elem.show(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `slideDown` special effect
            case "slideDown":

              elem.slideDown(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `fadeIn` special effect
            case "fadeIn":

              elem.fadeIn(self.options.showEffectSpeed);

              break;

              //If none of the above options were passed, then a `jQueryUI show effect` is expected
            default:

              elem.show();

              break;

          }

        }

        // If the current subheader parent element is a header
        if (elem.parent().is(headerClass)) {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem));

        }

        // If the current subheader parent element is not a header
        else {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem.closest(headerClass).find(subheaderClass).not(elem.siblings())));

        }

        // Maintains chainablity
        return self;

      },

      // Hide
      // ----
      //      Closes the current sub-header
      hide: function(elem) {

        // Stores the plugin context in the `self` variable
        var self = this;

        //Determines what jQuery effect to use
        switch (self.options.hideEffect) {

          // Uses `no effect`
          case "none":

            elem.hide();

            break;

            // Uses the jQuery `hide` special effect
          case "hide":

            elem.hide(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `slideUp` special effect
          case "slideUp":

            elem.slideUp(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `fadeOut` special effect
          case "fadeOut":

            elem.fadeOut(self.options.hideEffectSpeed);

            break;

            // If none of the above options were passed, then a `jqueryUI hide effect` is expected
          default:

            elem.hide();

            break;

        }

        // Maintains chainablity
        return self;
      },

      // _triggerShow
      // ------------
      //      Determines what elements get shown on scroll and click
      _triggerShow: function(elem, scroll) {

        var self = this;

        // If the current element's parent is a header element or the next element is a nested subheader element
        if (elem.parent().is(headerClass) || elem.next().is(subheaderClass)) {

          // Shows the next sub-header element
          self.show(elem.next(subheaderClass), scroll);

        }

        // If the current element's parent is a subheader element
        else if (elem.parent().is(subheaderClass)) {

          // Shows the parent sub-header element
          self.show(elem.parent(), scroll);

        }

        // Maintains chainability
        return self;

      },

      // _addCSSClasses
      // --------------
      //      Adds CSS classes to the newly generated table of contents HTML
      _addCSSClasses: function() {

        // If the user wants a jqueryUI theme
        if (this.options.theme === "jqueryui") {

          this.focusClass = "ui-state-default";

          this.hoverClass = "ui-state-hover";

          //Adds the default styling to the dropdown list
          this.element.addClass("ui-widget").find(".toc-title").addClass("ui-widget-header").end().find("li").addClass("ui-widget-content");

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap") {

          this.element.find(headerClass + "," + subheaderClass).addClass("nav nav-list");

          this.focusClass = "active";

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap3") {

          this.element.find(headerClass + "," + subheaderClass).addClass("list-group");

          this.element.find(itemClass).addClass("list-group-item");

          this.focusClass = "active";

        }

        // If a user does not want a prebuilt theme
        else {

          // Adds more neutral classes (instead of jqueryui)

          this.focusClass = tocFocusClassName;

          this.hoverClass = tocHoverClassName;

        }

        //Maintains chainability
        return this;

      },

      // setOption
      // ---------
      //      Sets a single Tocify option after the plugin is invoked
      setOption: function() {

        // Calls the jQueryUI Widget Factory setOption method
        $.Widget.prototype._setOption.apply(this, arguments);

      },

      // setOptions
      // ----------
      //      Sets a single or multiple Tocify options after the plugin is invoked
      setOptions: function() {

        // Calls the jQueryUI Widget Factory setOptions method
        $.Widget.prototype._setOptions.apply(this, arguments);

      },

      // _scrollTo
      // ---------
      //      Scrolls to a specific element
      _scrollTo: function(elem) {

        var self = this,
          duration = self.options.smoothScroll || 0,
          scrollTo = self.options.scrollTo,
          currentDiv = $('div[data-unique="' + elem.attr("data-unique") + '"]');

        if (!currentDiv.length) {

          return self;

        }

        // Once all animations on the page are complete, this callback function will be called
        $("html, body").promise().done(function() {

          // Animates the html and body element scrolltops
          $("html, body").animate({

            // Sets the jQuery `scrollTop` to the top offset of the HTML div tag that matches the current list item's `data-unique` tag
            "scrollTop": currentDiv.offset().top - ($.isFunction(scrollTo) ? scrollTo.call() : scrollTo) + "px"

          }, {

            // Sets the smoothScroll animation time duration to the smoothScrollSpeed option
            "duration": duration

          });

        });

        // Maintains chainability
        return self;

      }

    });

  })); //end of plugin
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+</head>
 
-<h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
-<h4 class="author"><em>Johannes Ranke</em></h4>
-<h4 class="date"><em>2018-01-14</em></h4>
-
-</div>
+<body>
+<h1>Laboratory Data L1</h1>
 
+<p>The following code defines example dataset L1 from the FOCUS kinetics
+report, p. 284:</p>
 
-<div id="laboratory-data-l1" class="section level1">
-<h1>Laboratory Data L1</h1>
-<p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p>
-<pre class="r"><code>library(&quot;mkin&quot;, quietly = TRUE)
+<pre><code class="r">library(&quot;mkin&quot;, quietly = TRUE)
 FOCUS_2006_L1 = data.frame(
   t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2),
   parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6,
              72.0, 71.9, 50.3, 59.4, 47.0, 45.1,
              27.7, 27.3, 10.0, 10.4, 2.9, 4.0))
-FOCUS_2006_L1_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L1)</code></pre>
-<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>&quot;SFO&quot;</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>
-<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:    0.9.47.1 
+FOCUS_2006_L1_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L1)
+</code></pre>
+
+<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>&quot;SFO&quot;</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>
+
+<pre><code class="r">m.L1.SFO &lt;- mkinfit(&quot;SFO&quot;, FOCUS_2006_L1_mkin, quiet = TRUE)
+summary(m.L1.SFO)
+</code></pre>
+
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 17:50:05 2018 
-## Date of summary: Sun Jan 14 17:50:05 2018 
+## Date of fit:     Thu Mar  1 14:24:54 2018 
+## Date of summary: Thu Mar  1 14:24:54 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.242 s
+## Fitted with method Port using 37 model solutions performed in 0.24 s
 ## 
 ## Weighting: none
 ## 
@@ -321,36 +311,46 @@ summary(m.L1.SFO)</code></pre>
 ##    21   parent     10.0    12.416  -2.4163
 ##    21   parent     10.4    12.416  -2.0163
 ##    30   parent      2.9     5.251  -2.3513
-##    30   parent      4.0     5.251  -1.2513</code></pre>
+##    30   parent      4.0     5.251  -1.2513
+</code></pre>
+
 <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p>
-<pre class="r"><code>plot(m.L1.SFO, show_errmin = TRUE, main = &quot;FOCUS L1 - SFO&quot;)</code></pre>
-<p><img 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" /><!-- --></p>
+
+<pre><code class="r">plot(m.L1.SFO, show_errmin = TRUE, main = &quot;FOCUS L1 - SFO&quot;)
+</code></pre>
+
+<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/></p>
+
 <p>The residual plot can be easily obtained by</p>
-<pre class="r"><code>mkinresplot(m.L1.SFO, ylab = &quot;Observed&quot;, xlab = &quot;Time&quot;)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p>
-<pre class="r"><code>m.L1.FOMC &lt;- mkinfit(&quot;FOMC&quot;, FOCUS_2006_L1_mkin, quiet=TRUE)</code></pre>
-<pre><code>## Warning in mkinfit(&quot;FOMC&quot;, FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge.
-## Convergence code is 1</code></pre>
-<pre class="r"><code>plot(m.L1.FOMC, show_errmin = TRUE, main = &quot;FOCUS L1 - FOMC&quot;)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre>
-<pre><code>## mkin version:    0.9.47.1 
+
+<pre><code class="r">mkinresplot(m.L1.SFO, ylab = &quot;Observed&quot;, xlab = &quot;Time&quot;)
+</code></pre>
+
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbAAAAFoCAMAAAAB/V5aAAAC2VBMVEUAAAABAQEDAwMICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAyMjI0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKkpKSlpaWmpqanp6eoqKipqamqqqqsrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7///8otsV2AAAACXBIWXMAAAsSAAALEgHS3X78AAAOo0lEQVR4nO2d/V9UVR7H71riAxKk5lrbg+1m5VpbggoImiZqmvmADwklkFSoKWlpYtq2Wmab2pZraW6pWaZb65q1hWWZLmqgkWKuhgQCOoM8CMz5C/YOw0MMM3i+92HufGc+7x/O63L83u85c94OnHvvufcqArBCsboDgAaEMQPCmAFhzIAwZkAYMyCMGRDGDAhjBoQxA8KYAWHMgDBmQBgzIIwZEMYMCGMGhDEDwpgBYcyAMGZAGDMgjBkQxgwIYwaEMQPCmAFhzIAwZkAYMyCMGRDGDAhjBoQxA8KYAWHMgDBmQBgzIIwZEMYMCGMGhDEDwpgBYcyAMGZAGDMgjBkQxgwIYwaEMQPCmAFhzIAwZkAYMyCMGRDGDAhjhg5hJduACbx/2SxhWxPfAMYTddI0YWu17wu88giE8QLCmAFhzIAwZkAYMyCMGRDGDAhjBoQxA8LMZny8Hm5xTwdhZhNj7N4QZjYQxgwIYwaEMQPCmAFhzIAwZkAYMyCMGRDGjJYhL9xfJr9bnPvejUCY2TQPecboBXGvSu+muO3dBISZTdOQb12kFhNzmqqVVd3/dEKI7X279F6n/rS5hy2td6/EcnVz6x9vek2MUfq12rsZCDObpiGflacWHzR/xZTFtfNGCXHnyrqDV6s/peembKksz0pQN//i+OwqfMOso2nIn92nFuvfaapWCsQP3YRw5G5NUwdZKRYRikq4umlrGHYIs4qmIc+LOyeOR51vqlaFnewqRNKknT+79IQVCFFx2jXkEGYhzUO+f1TspB+bq5VFtZkjheiS63hLqXWO87SkipJhM1uEXW69dxMQZjZejsOUheH3qGO/Kvz2FUOnO8e57OHu3abYmoWN6O15bwgzG2/CNO4NYWYDYczwIuw9jXtDmNngXCIzIIwZEMYMCGMGhDEDwpgRU6CHe9zTQZjZrEvRw2z3dBDGDAhjBoQxA8KYAWHMgDBmQBgzIIwZ+oWlVhVNCO2ZbnOvhzBT0C9MsY+aXliakexeD2GmYISwkFIhaiKaK953PWHihmF6+wY8YICws/0PC5F3k3v97ImaOwW8o19YZFinWPF1xBr3eggzBQNmiY5zOeJQtsO9GsJMwbxpPYSZAoQxA8KYAWHMgDBmQBgzIIwZEMYMCGMGhDEDwpgBYcwIJmGOfVt+vHKUnxNEwqoT5q576Hmre6GXIBK2cqNaTMq1uhs6CSJhE4vUYuPbVndDJ0EkbM4BtcjaY3U3dBJEwvIHbdi0Zkj77632f4JIWHX0nbffttDqXugliISt3KQWk763uhs6CSJhmHS0j98Jw6SjffxOWP7AnMoPMOnwiqyw2k1z11Rob4bAD6n3L7nok5ZMxCph9W+OSNjsXHxaN3LRqmUD2Y+jz7BK2MKsSxczXlY3dkWPW5U0eIX2doIMi4TVD24qMuLVIjNaeztBhkXCLo52lkPqhUgdr26sHKC9nSDDql+J0SVCnB6hbrzX7/l9a/s9o72dIMMqYd9GvbA0yvlyC1vUssXLIk+5/XNZjfaGAxvLpvW2f++tbNgofDI26XjrfzsaO2ZIKvcDJpOw/jisLbVRZ4VYn6W96UDGH4V9/7haOGK1Nx3IyAhTmiBl1i7seJpa1MVp3T2wkfyGvTyjqGjGRlJm7cLqo3OFY/nLWncPbCSF3VAhRMXNpMw6Tv6eGhsbtbRe8+4BjaSwcJt6sHsNKbNHYbnJwxdfIKUBrZEUNnXKuXOJU0iZPQnLG/x99UdDcIylA0lhpdPCwqeVkjJ7EvbEIbVY8ikpD2iFb6f140vU4q1N2pMCSWHlCdecntnmgW3t4knYCqerh445N48sf5H/QncLkBSWmKVUDJtKyuxJWPXI2WvG/dm59e7oPZ8M3U1KCJxICrvugiKKDZglOvZvczU4QJ152HEVjI6ksF5ViqjoQcrc7nFY+QPOcgiOtchICkvZqhQkzSBlbv/AecBlIS7hG0ZHUpgtJSJsWhkpc/vCNo3N3jt8FykhcCIpbGH7YZ64wqmpQ4uX5pFzAllhT3SP21JNy+x3C0kDA9kD59pPEq+fQ8oMYaYgfabjl3V3X0fKDGGmIClsfXy3GXvrSJk9CnszKnbod6Q0oDWSwhK2VFIzexK265FaUTToPDUVaEFSWA/6jM6TsBmn1OLVD8i5QDOSwjanF1MzexI29YxarN1GTQVakBRm0CKcrXMdwhZTRMoDWuHjZW7LI8cO/Ex7TmCAsLIFfTp37DOvzR1enqf19bTzW8Ad/RcwE9K/KC7Onj/GvR7HYaag/wJmaMOaGsdvmys+db0y+o7rU1IOifwUlIaWf2j/uRUSFzBHqt+wkn3zE5oryg42MDn+4MEKUX0QpaHl2BNSwtq5gFmW2Sek481z26w2NPZXYt1LMbFLiaefAxKrLmBSWfxCveONNCMzMsWqC5hUGi5Ox+GeMb+83cgDrpuPRuPpELLCVsfXPxC6npTZ2G/Y2Dwh/oc7kKSFheV/nJzTnZTZWGGnox97ciDWFEgLu/bIuG9PGbAuUTv1uYdrDU3IFElh67o+LH73V1JmnOkwBSaTDtCET4QVJsXGb9eeCfwKSWH5w7tc88ApUuYWYZdjvhUVif+kdg14QlLYXZn5J+bRHgjVIuzAfLUoeZDatSDixNOpW9q8CNszsvc4qzO0y9eSOtEibM9ytai+j7R3UHFocPbxJZLn3SSFZS0qKFj0IqkXLcJKB1cL8fYLpL2DigedS13GnZWK9cmDVXZGzRmfiFvRvRLjLJ77UirWN9P6yv+e0Z4o8Jl+RAjH0BKpWClh9sx+If2fJi4lxXGYNKfv/duOyS/JxcoIs92SdNh+JLkfbWkihMlj27j6sGSojLC5M12huHvFD5AR1rfhOQ0i12fPmgLekRHWsaphu+pqUuYAFWaTPMA1C8I3LO8OUuaAFPblwDEDrD2glPobluIKnUfKHIjCSgZdEI6n3rOyC3KzxEePVhxN7qP/0UV6OE++f8Z4dryiFoWTreyC1HGYbW7fjn2fovkyWFjxyLET4n8yMqMWdjofk3qW9hhCg+FyAXPyfvWP6P1GZtRC2cCii5dTPzQ879ux0TN/kQtlIsy1zC3BbmBKTbwZGt55tOFZN6dWi+x4uec4MRHmOj96n9VrtYsHX6xzzH/X6LQjLqnFY8ekYrkIy9ig/smnPf/PBD5crRbnJhmdtuEhaQu+kYrlIqxqfnT0LMsX/u5yTjrOGP7/ZuE2IeyRl6RiuQjzD9RJh6hN3WF02srxDz0e+R+5WAgj8d3QuMjXTch7Nlf2+i6EEbH6mZwQxgwIYwaEMQPCmAFhzIAwZkAYMyCMGRDGDAhjBoQxA8KYAWHMgDBm+EbY5R9oLzwFXvGJsD2RM0cm4zk2huALYeWDLgnx+grtqUALvhC293m1qBquPRVowRfCvlmgFqVjtacCLfhCWM2gI6I6GW9cMQSfTDp+mhwT8472TOBX4DiMGRDGDAhjhn5hqVVFE0J7pre53Q/CTEG/MMU+anphaUayez2EmYIRwkJK1al7hHs9hJmCAcLO9j8sRN5NzRX2ggaSxuvtG/CAfmGRYZ1ixdcRa5ordrteR3Wv5bffBSQGzBId53LEoew2D4jZulZrn0A7mDethzBTMEaYpzAIMwUIY4Yxwm71UAdhpoC/YcyAMGZAGDMgjBkQxgwIYwaEMQPCmAFhzIAwZkAYMyCMGRDGDAhjBoQxA8KY4Z/Ccl7ZUK5974DGL4W9MvGjDfee0N50IOOPwi7EOITIx8Jhj/ijsAOZzjJGe9OBjD8KK05QiyLj30kTEPijMJHxzLF9MQe0Nx3I+KUwx/tpC/K1txzQ+KUw4B0IYwaEMQPCmAFhzIAwZkAYMyCMGRDGDAhjBoQxA8KYAWHMgDBmQBgzIIwZEMYMCGMGhDEDwpgBYcyAMGaYJ2x3v/gWIrqFkQgJpcV36kqL79yFFt+lMy2+awgtPrRHvCS3/myWsFZMPUOLn51Di1+2hxb/xj9o8dtfpcVnL6bFH0+jxXsFwlxA2BWAMI1AmAsIuwIQphEIcxF0wqa3Pxltw9wjtPg/f0aL//sWWvyONVeO+TVfLaHF58+ixXvFKGFtXlZ1BextXjTRPpfqaPHVNbT42ipafH0FLV7YifHeMEoY8BEQxgwIYwaEMQPCmAFhzIAwZkAYMyCMGcYIKxvVbVQZIX6Aoiip0tGVvWhtuOKl29h+W9dBxwn5G+Ol8//r911v200fIy8YI+zpWdWzFsiHO8LO2u3S54JW362Q2nDFS7dxqtP+ykX3yOdvjJfOXx+2t357T/IYecMYYX2+EF/0kQ8/16l/p9FFstGf71JIbbjipdv4/FEhCiPk8zfGS+ev+dhh29mXPEbeMEZYSLEo7iwfnhObc37aBPl4hdiGQmyjLj2DlN8ZT8hvV37zNXmMvGGMsI4lorgjbZfCUPlYhdhG04eSbOPzuxfVU/K74uXzi4rld2kaI08YI+zmfSL7FvnwQ+p/uNKe8vEKsQ2F0oZjYcxJSv7GeOn8p9LV388h5DHyhjHC5mQ6MufJh2dfe6xmNuGKnkJsQ6G08dWtF+x2u3z+xnjp/NUdv6x8LoY8Rt4wRlj5/b1GXZAPd7x2Y0QS4ZKnQmxDobSxTHEin78xXv4z7L4jNO4keYy8gQNnZkAYMyCMGRDGDAhjBoQxA8KYAWHMgDBmQBgzIIwZEMYMCGMGhDEDwpgBYcyAMGZAGDMgjBkQxozAFXZnhw5Khw79A+0TBtjHaY1rTb7VvTCWwBcWYATiZ2pGaSyUpT2eefbG0BeFLa13r8Ryq7uli+AQ9u5RJavm06tEypbK8qwEq7uli+AQVuNQKoVDERHORbvhVndLF8EhzLWpiLACISpOW9wrfQSXsGlJFSXDZlrcK30El7Cyh7t3m0J98Jx/EdDCAhEIYwaEMQPCmAFhzIAwZkAYMyCMGRDGDAhjBoQxA8KYAWHMgDBmQBgzIIwZEMYMCGMGhDHj/7pMju4uzBEuAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-5"/></p>
+
+<p>For comparison, the FOMC model is fitted as well, and the \(\chi^2\) error level
+is checked.</p>
+
+<pre><code class="r">m.L1.FOMC &lt;- mkinfit(&quot;FOMC&quot;, FOCUS_2006_L1_mkin, quiet=TRUE)
+plot(m.L1.FOMC, show_errmin = TRUE, main = &quot;FOCUS L1 - FOMC&quot;)
+</code></pre>
+
+<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-6"/></p>
+
+<pre><code class="r">summary(m.L1.FOMC, data = FALSE)
+</code></pre>
+
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 17:50:06 2018 
-## Date of summary: Sun Jan 14 17:50:06 2018 
-## 
-## 
-## Warning: Optimisation by method Port did not converge.
-## Convergence code is 1 
-## 
+## Date of fit:     Thu Mar  1 14:24:56 2018 
+## Date of summary: Thu Mar  1 14:24:57 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 155 model solutions performed in 0.432 s
+## Fitted with method Port using 611 model solutions performed in 1.376 s
 ## 
 ## Weighting: none
 ## 
@@ -370,16 +370,16 @@ summary(m.L1.SFO)</code></pre>
 ## None
 ## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
-##           Estimate Std. Error   Lower  Upper
-## parent_0     92.47      1.449   89.38  95.56
-## log_alpha    11.35    435.800 -917.60 940.30
-## log_beta     13.70    435.800 -915.20 942.60
+##           Estimate Std. Error    Lower   Upper
+## parent_0     92.47      1.482    89.31   95.63
+## log_alpha    11.25    598.200 -1264.00 1286.00
+## log_beta     13.60    598.200 -1261.00 1289.00
 ## 
 ## Parameter correlation:
 ##           parent_0 log_alpha log_beta
-## parent_0    1.0000    0.2209   0.2208
-## log_alpha   0.2209    1.0000   1.0000
-## log_beta    0.2208    1.0000   1.0000
+## parent_0    1.0000   -0.3016  -0.3016
+## log_alpha  -0.3016    1.0000   1.0000
+## log_beta   -0.3016    1.0000   1.0000
 ## 
 ## Residual standard error: 3.045 on 15 degrees of freedom
 ## 
@@ -388,9 +388,9 @@ summary(m.L1.SFO)</code></pre>
 ## t-test (unrealistically) based on the assumption of normal distribution
 ## for estimators of untransformed parameters.
 ##           Estimate  t value    Pr(&gt;t) Lower Upper
-## parent_0     92.47 63.33000 6.183e-20 89.38 95.56
-## alpha     85190.00  0.03367 4.868e-01  0.00   Inf
-## beta     891000.00  0.03367 4.868e-01  0.00   Inf
+## parent_0     92.47 64.45000 4.757e-20 89.31 95.63
+## alpha     76830.00  0.02852 4.888e-01  0.00   Inf
+## beta     803500.00  0.02852 4.888e-01  0.00   Inf
 ## 
 ## Chi2 error levels in percent:
 ##          err.min n.optim df
@@ -399,50 +399,100 @@ summary(m.L1.SFO)</code></pre>
 ## 
 ## Estimated disappearance times:
 ##         DT50  DT90 DT50back
-## parent 7.249 24.08    7.249</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"><em>χ</em><sup>2</sup></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"><em>χ</em><sup>2</sup></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"><em>χ</em><sup>2</sup></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"><em>χ</em><sup>2</sup></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>
-</div>
-<div id="laboratory-data-l2" class="section level1">
+## parent 7.249 24.08    7.249
+</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 \(\chi^2\) 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 \(\chi^2\) 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 \(\chi^2\) 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
+\(\chi^2\) error levels was compared with KinGUII, CAKE and DegKin manager in
+a project sponsored by the German Umweltbundesamt [@ranke2014].</p>
+
 <h1>Laboratory Data L2</h1>
-<p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p>
-<pre class="r"><code>FOCUS_2006_L2 = data.frame(
+
+<p>The following code defines example dataset L2 from the FOCUS kinetics
+report, p. 287:</p>
+
+<pre><code class="r">FOCUS_2006_L2 = data.frame(
   t = rep(c(0, 1, 3, 7, 14, 28), each = 2),
   parent = c(96.1, 91.8, 41.4, 38.7,
              19.3, 22.3, 4.6, 4.6,
              2.6, 1.2, 0.3, 0.6))
-FOCUS_2006_L2_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L2)</code></pre>
-<div id="sfo-fit-for-l2" class="section level2">
+FOCUS_2006_L2_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L2)
+</code></pre>
+
 <h2>SFO fit for L2</h2>
-<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>
-<pre class="r"><code>m.L2.SFO &lt;- mkinfit(&quot;SFO&quot;, FOCUS_2006_L2_mkin, quiet=TRUE)
+
+<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>
+
+<pre><code class="r">m.L2.SFO &lt;- mkinfit(&quot;SFO&quot;, FOCUS_2006_L2_mkin, quiet=TRUE)
 plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,
-     main = &quot;FOCUS L2 - SFO&quot;)</code></pre>
-<p><img 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" /><!-- --></p>
-<p>The <span class="math inline"><em>χ</em><sup>2</sup></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>
-<p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p>
-</div>
-<div id="fomc-fit-for-l2" class="section level2">
+     main = &quot;FOCUS L2 - SFO&quot;)
+</code></pre>
+
+<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-8"/></p>
+
+<p>The \(\chi^2\) 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>
+
+<p>We may add that it is difficult to judge the random nature of the residuals just
+from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a
+priori</em> why a consistent underestimation after the approximate DT90 should be
+irrelevant. However, this can be rationalised by the fact that the FOCUS fate
+models generally only implement SFO kinetics.</p>
+
 <h2>FOMC fit for L2</h2>
-<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p>
-<pre class="r"><code>m.L2.FOMC &lt;- mkinfit(&quot;FOMC&quot;, FOCUS_2006_L2_mkin, quiet = TRUE)
+
+<p>For comparison, the FOMC model is fitted as well, and the \(\chi^2\) error level
+is checked.</p>
+
+<pre><code class="r">m.L2.FOMC &lt;- mkinfit(&quot;FOMC&quot;, FOCUS_2006_L2_mkin, quiet = TRUE)
 plot(m.L2.FOMC, show_residuals = TRUE,
-     main = &quot;FOCUS L2 - FOMC&quot;)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
-<pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
-<pre><code>## mkin version:    0.9.47.1 
+     main = &quot;FOCUS L2 - FOMC&quot;)
+</code></pre>
+
+<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/></p>
+
+<pre><code class="r">summary(m.L2.FOMC, data = FALSE)
+</code></pre>
+
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 17:50:07 2018 
-## Date of summary: Sun Jan 14 17:50:07 2018 
+## Date of fit:     Thu Mar  1 14:24:57 2018 
+## Date of summary: Thu Mar  1 14:24:57 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 81 model solutions performed in 0.166 s
+## Fitted with method Port using 81 model solutions performed in 0.169 s
 ## 
 ## Weighting: none
 ## 
@@ -491,21 +541,31 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50back
-## parent 0.8092 5.356    1.612</code></pre>
-<p>The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></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 id="dfop-fit-for-l2" class="section level2">
+## parent 0.8092 5.356    1.612
+</code></pre>
+
+<p>The error level at which the \(\chi^2\) 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>
+
 <h2>DFOP fit for L2</h2>
-<p>Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level.</p>
-<pre class="r"><code>m.L2.DFOP &lt;- mkinfit(&quot;DFOP&quot;, FOCUS_2006_L2_mkin, quiet = TRUE)
+
+<p>Fitting the four parameter DFOP model further reduces the \(\chi^2\) error level.</p>
+
+<pre><code class="r">m.L2.DFOP &lt;- mkinfit(&quot;DFOP&quot;, FOCUS_2006_L2_mkin, quiet = TRUE)
 plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
-     main = &quot;FOCUS L2 - DFOP&quot;)</code></pre>
-<p><img 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" /><!-- --></p>
-<pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
-<pre><code>## mkin version:    0.9.47.1 
+     main = &quot;FOCUS L2 - DFOP&quot;)
+</code></pre>
+
+<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/></p>
+
+<pre><code class="r">summary(m.L2.DFOP, data = FALSE)
+</code></pre>
+
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 17:50:08 2018 
-## Date of summary: Sun Jan 14 17:50:08 2018 
+## Date of fit:     Thu Mar  1 14:24:58 2018 
+## Date of summary: Thu Mar  1 14:24:58 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -514,7 +574,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 336 model solutions performed in 0.712 s
+## Fitted with method Port using 336 model solutions performed in 0.721 s
 ## 
 ## Weighting: none
 ## 
@@ -542,8 +602,12 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## log_k2    -1.0880         NA    NA    NA
 ## g_ilr     -0.2821         NA    NA    NA
 ## 
-## Parameter correlation:</code></pre>
-<pre><code>## Warning in print.summary.mkinfit(x): Could not estimate covariance matrix; singular system:</code></pre>
+## Parameter correlation:
+</code></pre>
+
+<pre><code>## Warning in print.summary.mkinfit(x): Could not estimate covariance matrix; singular system:
+</code></pre>
+
 <pre><code>## Could not estimate covariance matrix; singular system:
 ## 
 ## Residual standard error: 1.732 on 8 degrees of freedom
@@ -565,36 +629,62 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50_k1 DT50_k2
-## parent 0.5335 5.311 0.03009   2.058</code></pre>
-<p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p>
-</div>
-</div>
-<div id="laboratory-data-l3" class="section level1">
+## parent 0.5335 5.311 0.03009   2.058
+</code></pre>
+
+<p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the
+chi<sup>2</sup> error level criterion. However, the failure to calculate the covariance
+matrix indicates that the parameter estimates correlate excessively. Therefore,
+the FOMC model may be preferred for this dataset.</p>
+
 <h1>Laboratory Data L3</h1>
-<p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p>
-<pre class="r"><code>FOCUS_2006_L3 = data.frame(
+
+<p>The following code defines example dataset L3 from the FOCUS kinetics report,
+p. 290.</p>
+
+<pre><code class="r">FOCUS_2006_L3 = data.frame(
   t = c(0, 3, 7, 14, 30, 60, 91, 120),
   parent = c(97.8, 60, 51, 43, 35, 22, 15, 12))
-FOCUS_2006_L3_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L3)</code></pre>
-<div id="fit-multiple-models" class="section level2">
+FOCUS_2006_L3_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L3)
+</code></pre>
+
 <h2>Fit multiple models</h2>
-<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>
-<pre class="r"><code># Only use one core here, not to offend the CRAN checks
+
+<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>
+
+<pre><code class="r"># Only use one core here, not to offend the CRAN checks
 mm.L3 &lt;- mmkin(c(&quot;SFO&quot;, &quot;FOMC&quot;, &quot;DFOP&quot;), cores = 1,
                list(&quot;FOCUS L3&quot; = FOCUS_2006_L3_mkin), quiet = TRUE)
-plot(mm.L3)</code></pre>
-<p><img 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" /><!-- --></p>
-<p>The <span class="math inline"><em>χ</em><sup>2</sup></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"><em>χ</em><sup>2</sup></span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level considerably.</p>
-</div>
-<div id="accessing-mmkin-objects" class="section level2">
+plot(mm.L3)
+</code></pre>
+
+<p><img 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" alt="plot of chunk unnamed-chunk-12"/></p>
+
+<p>The \(\chi^2\) 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 \(\chi^2\) test passes of 7%.  Fitting the four
+parameter DFOP model further reduces the \(\chi^2\) error level
+considerably.</p>
+
 <h2>Accessing mmkin objects</h2>
-<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>
-<pre class="r"><code>summary(mm.L3[[&quot;DFOP&quot;, 1]])</code></pre>
-<pre><code>## mkin version:    0.9.47.1 
+
+<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>
+
+<pre><code class="r">summary(mm.L3[[&quot;DFOP&quot;, 1]])
+</code></pre>
+
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 17:50:08 2018 
-## Date of summary: Sun Jan 14 17:50:08 2018 
+## Date of fit:     Thu Mar  1 14:24:59 2018 
+## Date of summary: Thu Mar  1 14:24:59 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -603,7 +693,7 @@ plot(mm.L3)</code></pre>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 137 model solutions performed in 0.291 s
+## Fitted with method Port using 137 model solutions performed in 0.283 s
 ## 
 ## Weighting: none
 ## 
@@ -668,40 +758,64 @@ plot(mm.L3)</code></pre>
 ##    30   parent     35.0     35.15 -0.14707
 ##    60   parent     22.0     23.26 -1.25919
 ##    91   parent     15.0     15.18 -0.18181
-##   120   parent     12.0     10.19  1.81395</code></pre>
-<pre class="r"><code>plot(mm.L3[[&quot;DFOP&quot;, 1]], show_errmin = TRUE)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></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"><em>χ</em><sup>2</sup></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>
-</div>
-</div>
-<div id="laboratory-data-l4" class="section level1">
+##   120   parent     12.0     10.19  1.81395
+</code></pre>
+
+<pre><code class="r">plot(mm.L3[[&quot;DFOP&quot;, 1]], show_errmin = TRUE)
+</code></pre>
+
+<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-13"/></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 \(\chi^2\) 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>
+
 <h1>Laboratory Data L4</h1>
-<p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p>
-<pre class="r"><code>FOCUS_2006_L4 = data.frame(
+
+<p>The following code defines example dataset L4 from the FOCUS kinetics
+report, p. 293:</p>
+
+<pre><code class="r">FOCUS_2006_L4 = data.frame(
   t = c(0, 3, 7, 14, 30, 60, 91, 120),
   parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0))
-FOCUS_2006_L4_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L4)</code></pre>
+FOCUS_2006_L4_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L4)
+</code></pre>
+
 <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p>
-<pre class="r"><code># Only use one core here, not to offend the CRAN checks
+
+<pre><code class="r"># Only use one core here, not to offend the CRAN checks
 mm.L4 &lt;- mmkin(c(&quot;SFO&quot;, &quot;FOMC&quot;), cores = 1,
                list(&quot;FOCUS L4&quot; = FOCUS_2006_L4_mkin),
                quiet = TRUE)
-plot(mm.L4)</code></pre>
-<p><img 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" /><!-- --></p>
-<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:    0.9.47.1 
+plot(mm.L4)
+</code></pre>
+
+<p><img 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8OnEV4mXZ8TMb6Yzv2m8Pn3PE583BrZPzr4gniOo3OCZ0Qdg+8THjfFiPfg49zhORBONPhEcXJw4psKU+dKRQX4iHhufj1yT8j0jyaTdcK+4y2LZHqjam9PmDszlSwRL4ATX5c/MkOmu8+Ih3BR9kd/7JP+T5J9Paz4Zrk7iCJrt+7X/aQxHpvkxYmVghM/vED205IPiW9fO2snLH9u0KRPFP9VHD/V+/YnGrhvwIh4Dz7N4sQXSYVc8SHxHZxeGpFUoOzYjiY7d0c/9M6OXGQDXPs39d1x4mdaZK8Y6oPiOb57I27om5hjursnPfMz/12LY/Wvp64OFd9j2xDCIfxsofruOPH+/IeKSx+OCcs+IR37etg3u+wnFWDe8fi/TNpx1B5hg4fldoF14YWMD0NPqe/u++fcqaZpZ0bw9H9IXGPnnQ3QcWc0jHj3v0wiTjuLvgw3v6xBtuTse+87D3qTHat3w5fFc9iOLwpLeu9nUevh8a1nw/gHGPHuf5kUnnbmOBfh1ujoPLj/zqCAl6B1JP/Yn75G+vOxekV8v2pURPYxl72VNVGJ9kmSaOcOddqZ9ZLHKXoDfz9Wr4imT2eETC50P2kLvXPX5O3TznSHgmP1yjidNzTm5ROuH1Mw7/gnjw98RmZE/6ldCpx42oq/VvR46KPrBWfHYcQnla9IlBnOr2p3AyeexuLPvBEf9te9zuO6OPGvWMbKDOV3tbuAFU9n8c275kbW2h/hpvqY7bNlxvHH2m+CnerNWbxSaKzdx9bO6QVGvP8fw5ABJ/6tOwNXu7Z7be2cPmDE03EMAwN+QcWZ+1zbDV47pzcY8dQcw0CBE59eVTXdtV14EMO6hdIb8tih6hiGFGSLJnk6brrX9i6lt+BSCq3ipTDFTfc4Xux5/4ZAufWgNNbOu72S0f2uGaKzmIQ33bNDY/EcD5w/DCxyvWmsnZebvvhCbfY0UUBw0z07NBbPMUrJKQk01s5Xbb/1boxbiP6b7kGTL5O2ix8mFRVAY/FKobF2+1SfXVu7OF2mO43FK4XG2u07d2mBgelylxSlsXgELhc45KGxdipOtpRH9QUO7dBYOxPvjvAChy1/nTdv3ozf6pKUQTDxSnG5wOHJ48eP/7ObHjkZBROvGPQFDv0RJp4A1x8LjbUz8ZJ0dnlGY+1MvAJorJ2JVwCNtTPxCqCxdiZeATTWzsQrgMbamXgF0Fg7E68AGmtn4hVAY+1MvAJorJ2JVwCNtbO1cwqgsXafu++cPjDxYnzkvnN6w8SLQU/1N9fOtYzn76/aRae8DIFYfG/hEzOJd6G+rq7udJD863wXIvGmWD6GEm+KM00RmGL5GEq8Kc40RSFYPmbbU1paWthVh5QMg0w85lba/gjp7/gby8dal+Xk5Dx3h+YJGQiZeMyttP0RQ/fqT+y46snWtIZw584MZ5qi8eRky4WTc0NqyTanK+yQLQEenGxpDYNw3UqyzekKE68astpD2+FbH3iyOY1h4lVDVntBxIThhPdD1BUmXjWEtTfUyN7hx0iYeNXQWDsTrwAaa2fiFUBj7Uy8AmisnYlXgD61bwkZ+JIe44ph4lWjS+1NoS3w0S90GFgME68aXWr/4REIX/1Eh4HFMPGq0af2uHc/DlF2S2TPYOJVo0/t11ct+1GPccUw8aqhsXYmXgE01s7Eu/NlRlbA3dsEDTTWzsS781DJr7d9EypooLF2Jt6d2NKxm88OFDTQWDsT7055ZJdO/XY7HtvvHx9B4ZoCJl4BV+O1ysMbsNWyBLj+WMwkXrha1j7dhVM43aFxPdnSTOLdVsvSWLxSaKxdyWpZOzQWrxQaa1e8c0dj8UqhsXYmXgE01q5Y/PVe6Txpv/sfDD1wwd//P0ywdy9M8N6e6jc6zp72n4978qOTr73Xf6lI77/vQYZ63ouK9EH/LO5JTJdCunbF4uF3Fp5TA/Zi6IcLpqzEBF/OxAR3hqreaPJ2e9oWz850l639iZdUpLd8GjI0eDsqsjkK2WnSeoskkrUrF++gOQ4XjcAF5x7BBLcvwwSvJKje6KxyXJQMTO2YU2nQ6ZW8iAyNRp6h8eMjyE7P70eG3GHilcPEI2Hi3aFEfAv2HrRRuOC8Y5hgcR4meG2k6o0+/SUuSgam9teLkCF0erteRoYS61GRn8cjOy08iAy5QyoeXlYdbGjHBFuu6bPReisuSgh6S9dbVHRqRV84A1MTOnSlDd3LDWLxDDpg4k0KE29SmHiTwsSbFCbepBCK3xcWtg8Ryh/Tbw0mXtUZGTwZ278CGcxNTMxFBet7OjOSivNBmZzIQI2D3QiqbEzNyIrR5aqolVB88MEDwdIRW6zNEoCOt00FyOCQ0pJMZDCorCwIEfwqATgzkojzQZmcCEGMg90IsmxMzaiK0eWqqZVQ/G8uXURct97aAAui0fElRwEy2GVs57nI4McAFCGDwJmRZBzI5UQIYhzsRpBlY2pGV4wul7xWQvGd6y52RoSa0ibUI+Nl2dyWUMFOm7YFIIN9Nm++FxkEzowk40AmJ1JQ42A2gi4bUzO6YnS55LUSih9Q9jlqKknZj4kvBQD0RgX7na25G9kz6NuaIGQQOLcoGQcyOZGCGgezEXTZmJrRFaPLJa+VdOcuNvaAdORSUFRUFCbObQkV3D0wZCsyuDEkuBAZBM6MJONAPiciEOPIbARRNqZmdMXocslrZR/nTAoTb1KYeJPCxJsUJt6kMPEmhYk3KUy8SWHiTQoTb1KYeJPCxJsUJt6kMPEmhYk3KUy8SWHiTYrx4m1znpxj+EYZYowX3/ivr/sYvlGGGD3Eg1GjRl3JHDos4woEL0C4GMC6lGEDlneE1y24oDAZwXP7cgHneoIPxiZkw7ykPFhxWuvUzYMu4rn/07MhzM6EYAKEqQBOWw5tE75xRH8IX7BAYTI3nzuWCzjXE6xuLw+CPQ70sG7RPnfToIt4ACYE1kJ44S4Inq/65nkAgy45IkvGXQtxXDuiMOXxDyCYWcT9V9C/fwH/kE/G3jysrjnWEede+MbSpUv3OZcLdCxqeA2sh8t7LC9BXi/E55CZBDtedeOLsEGnjHQakxdfy4kvz321HMBunPiacxDuXfKC8ypB3QHoCoGF/y+wuiqI/853tDcX5BeuccRvjmlfLtCxqMFW3It73FCS1wN35RxfAj8JdtDU8VJhNx0z0mPMNP7f9RMQ2MZPsgE4KQfakvgLLFbGOq+2181iK+Ki/H+B1dWB/He+o735+sjURkccdrzjHcsFnAsGYmzVd3GPP7Zy070O6esBehLkpjr77PbUmDFPAfjOfY8BvmwA5w5MnMp945v1yUinMRvShsdMa+QLyOLP9k+MjnmFD+XNc76moP/kIkeFsGAAP9U7OtqbYcbcG3EnjuUCsJd9wcCG4Q8Wcv+GKuDyJH96x0tPgtxUZ5/dopedPA5gQGWlU3xJ2Tr+Ed+sW0bGcWHEs9VfGbpFHwE9CXJTnX12225ZMQjArpXVnO5mC4APvW/hxfPNumVkHJmlO+/52dAt+gjoSRA4Z7+nkxNnA7ii70wAp8QvAjAtKavvRmBv1i0jhglh4k0KE29SmHiTovi+cwy6UHLfOQdfHvdnSK7v6w6FtSu+79y13vP8mP64S6bLQmPt7L5zCqCxdnb7MQXQWLuinbsrdXV150bok5YxMPFilOzctcbzNxXW5CKB3oKJF6N45855G3Fb4nmN8zIEJl6M4p07p/iTSx/6UePEjICJF6N4584h/u2xb/7vnyeGRejzN2L9YOLFEIqPsMJDkb//13nsvd58ECLx2UHre91V6HxScfz48f2ReuRkFGTifwd4BA0O8VENcGtMQejnnPhLe37QOkX9IBI/shzsrP6L43HrjPT09Im/1SUpgyATXy+e3Rzi94Y+EnkocmvvWPhV6OKoHVrmpytE4pPbX7PZhgsanPs3fgrhVC8+Q8pZfCv3Nq/Izk94J7MCXot17+ajEIlfuxrC6A2CBlOJF+NafFvmA0fg5aGe5mQYnu3cMfECnu06fdBezzIyECZejGrxcPOD//YoH0Nh4sWoFw8rgv3nRA0mXowH4uHFYSs8yMdQmHgxnoiH7XMnN3LfXhoUdVh1WsbAxIvxSDyEWwZXws8ybZdC1GZlEEy8GA/Fw8rBf1+9DsKhTSqzMgi9xFd+48m4xqCPeNiUmTL4UMEodTkZhj7ibamTxumzllVLdBLPfa67f3LeVTUZGYg+4o/NgHDMd56MbAS6iYffxS23qkjISPQRXzYbwglnPRnZCPQTD625cTXkCRmJPuLbR8/KnOzJwIago3gIKwbl20gTMhKddu5sR054Mq4x6Coetjw/wpff9OzjnBitxENYHvam7/6m11Z826krngxnMB6cV88j/6++bemQCjWJGYGm4s8PSh/8qUfpGIr68+rtKJnuqobNu0aemBFoKn7RTnjNj87C0+a8ehnWB39MmpchEIl/PyCmHkY5Htv2lJaWFnZ1iT+7H7b50dmm6s+rt33mXjyKhlkjfPECs0Ti77cUpHaIb12ek5Pz3B0u8eqQnPi12uWmN+p37lpfcS8ezdfDnvG9S40SiU+ywbQDUYIG8Wx3+dNqLZIyCDLxud2L7u1eKGgg+EjzcfAKz65DoD1E4vNjG5pSggQNZvo4l1gGttQECxpIim9ZHrpd+auNgGznzsZ9Lr0oeG428d+GChrIir84O8anzs1gB3DEoMTnBG3tE6RyqrdTMylx2x6f+Ss9Ey9GuyN3bsz4/Z8evEzaSSeYeDH6ibeGwWP9QisJe+mEGcSfmPl8rVS70eJtoVb4ZvbIiacI++mCCcR/G/7FjiipgOFT/arY9LhGeDxxnA/86dIE4tfnc7vklyQChouHtaftf677KjV+D3FfjTGB+BMpbbXBUidFGC/+BjVPRmz07iEdE4iH+ZFxZVLtXhTPfa5fEpznzQO5ZhCPwqviIWxeM3jmGc+G8AAmXoxh4jn2Jcfv8NJJOky8GCPFQ3huQfBSr1wnj4kXY6x4CFv+Hpey2/gTcpl4MUaL5zg9J+Sl77UaTCFMvBgviOfe9pviEzY1azeePEy8GK+I5/j+5dAnDPzDLRMvxlviOcqeCF1cpfWgCIjECy/u2P7eqlWrlt2pS1IG4XPiIWzdnhLxuiHXxyQSL7y4o3VzYWFhfoDmCRmID4rnuLpuZOwK/T/hkU31iIs7+im+KZ6j7v34uBU/6TW6A/Y7XgwvHogvWAxVLaHygEtrRsW8qudCcyZejMP3tKqqma7tqpZQecC3X2wYOzhbt7/cM/FiHOL7Vlb2dW1XuYRKLfOTJqdYm3dmBD/xD10+3yPFS812btArfmVAV9G1CoVLqKSWEWlLLbcfPfsg98B2bGFY4mrtj+vh3/EbZXrTKx6P1MJBbfkpCcK/fuZ88uO7SWHz97ZqugGM+CkA9EUGHdAr/oOuRUWu7V9mZAXcvU3QoG/xaRnPjBCco9O6d37Yw29reHQHIz5xnSVLpje94jOBbYpr+0Mlv972jfqVNMScOCj+S/1PH6aGpBdKnjBMDkZ86OE1STK96RU/DlgGuLbHlo7dfHagoMEbxdsqXo0Pe6ZYg+uOYMRnlfwlTaY3veI3du+2zrW9PLJLp367BQ3eKr7t0JLYyPmfengFRfZxToxDfNyaRrnu3iy+ef/iuMisojr1I2DEm/rjXFNh6lyZ7t4uvuVQTsLg6R/x11RrfSPt74S9se94yyKZ3jdrv5A+5DXCTXsdnPi6/JEZMt29LZ7H+uWbj4SMe/3RnDNpG+RfLQQrvlnuRpI3ax+3t+1puU/9vgZO/PCC63LdfUG8nXMb744Ykj6E7CAPfqqfggw6uFl7BITHniHasvfBiS+SCrniM+IhnHSwaXbC2IGJOXsU7+9rtXM3cWtd2jbcS30QnPiZFtkTX31I/IWJEbNauIyKnhs26NE3D8vul0LtjtXXz3vYj6535QAn3n/3bKv/Pidm8JS3D8v8qsK8493/MumGt2tv8egsJZ89EcNjbJUbsuIGpS7f5b5I+Nisl+xL9jDi3f8yaey5CPL8IzQlukF9d7Jj9W74sHgHNZ9kPxyW8NymyvabbZVDvii077JjxLv/ZVJ4LoLjGn+dc3K2Q+tyL33t/UJOVp76ERKJjtW74fPi7Vze+7dpYWHTXt91wf501XrnfZKIdu6E5yI4rup5R2npaW4m8NLXP5eUrslWP8I0omP1bviHeDvtZzYtTBwY+1T+4Z1p1p/tf3BA79w1SZ12dvNcBDterj1vwhvBFvXdyY7Vu+FH4h00Hl+bNbx3QORx/gnmHf/k8YFyn8y9XfuJTRc86I0T7wfFq8T5WQ8jPql8RaLMIH5auxOceJMWbyfpFctYmd401u4Ub87i7TwZs322TG8aa3dO9eYsXik01k7DARwFYMTTcAwDC078W3cGrpbpTmPxdqg5hoECv6DizH2u7b522NJDMOLpOoYhAU58elXVdNd2o5dQ6QxGPI3HMFwgWzRp8BIqvWE7d2KU3IWq5ZHk5OSE23XKyxCQ4l/sef+GQLnSaBV/JaP7XTMkTmfo3fGgvq6u7nSQ+wv8B6T4B84fBrLHwWkVn774Qm32NNf2zuLpn8biOUYp+URLY+181cP4BzGu7VVTj7n+RGgsHpp8mbRd/DBRoG1JthnEK4LG2u1TfXZt7eJ0t9DhpcJnNBavFBprt+/cpQUGpsudqUxj8Uohq/3ctBGEyz30hR2rV8rvPNuxjTt6ddznJB10holXivAChy3T09PTU7sQ9LYOgXCbLy2wY+IVI7zAYY3FYjkSSNJ7aFnDWF+6vyoTrxqy2r9Pjy+Uf5VxMPEEuP5YaKydiZeks8szGmtn4hVAY+1MvAJorJ2JVwCNtTPxCqCxdiZeATTWzsQrgMbamXgF0Fg7E68AGmtn4hVAY+1MvAJorB19erVZVtIogMbaUeLNs5JGATTWjhLPVtIIoLF2JStp7NBYvFJorF3Jzl3LiOjo6DA6l1BJ8X5ATD2MEjSYSbwpikdwv6UglfraUeJNUTyCJBtMO+CsvTWe0tkOJV5YvB0Tic+PbU6I6A4AAARBSURBVGhK6VgjeoXSBaMo8S7F85hIPLRZIbwoeE5j7cidOzMUj4adbHkDGotHw062vAGNxSuFxtqZeAXQWDsTrwAaa2fiFUBj7Uy8Apy1nz+q5IZXPgcTrxpH7ZuHPBNCdptD34CJV42j9ohmWPq8Nuk4adN0NBRMvGqc4lvgrue0ScdOwYBB8zUcDgkTrxpH7YUxzwaf0yYdnsZB7TD9iHbjIWHiVeOs/adDWu7c/ZQCYZ6Cm/p6DBOvGn1qT3jtwxAPbiCpGCZeNfrU3vLRKvd7ouoAE68aGmtn4hVAY+1MvDvZQet73SW8cBWNtTPx7owsBzur/yJooLF2Jt6d5PbXbLbhjsf2U8vD79AlKYNg4pWydjWE0cLrEF+NR77WD2DiVWMm8S6rZSm+Jw0C1x+LmcQLV8u2jE9OTn64p055GYJnJ1uaSbzbalkai1cKjbUrXi1LY/FKobF2xTt3NBavFBprZ+IVQGPtisU33fUgzwO3/hrDr3DBW27DBDupHxcbvPWP9rR7fu3Jj06+9ls7qUgPUzOmplvQSfzhQSmka1cs3klzHC4agQvOxZ1usn0ZJnglQfVGZ5XjomRgan/1E2QInV7Ji8jQ6DpU5MdHkJ2e348MucPEK4eJR8LEu8PEM/Ei/Ep8azIuOhoXXPglJrjrLUywcbzqjT57ChclA1P7yn8iQ+j0DuQhQxOvoiIXH0N2eukYMuQOqXgGJTDxJoWJNylMvElh4k0KE29SmHiTQih+X1jYPkQof0y/NZh4VWdk8GRs/wpkMDcxMRcVrO/pzEgqzgdlciIDNQ52I6iyMTUjK0aXq6JWQvHBBw8ES0dssTZLADreNhUgg0NKSzKRwaCysiBE8KsE4MxIIs4HZXIiBDEOdiPIsjE1oypGl6umVkLxv7l0sYt0xNoAC6LR8SVHATLYZWznucjgxwAUIYPAmZFkHMjlRAhiHOxGkGVjakZXjC6XvFZC8Z3rLnZGhJrSJtQj42XZ3JZQwU6btgUgg302b74XGQTOjCTjQCYnUlDjYDaCLhtTM7pidLnktRKKH1D2OWoqSdmPiS8FAPRGBfudrbkb2TPo25ogZBA4tygZBzI5kYIaB7MRdNmYmtEVo8slr5V05y429oB05FJQVFQUJs5tCRXcPTBkKzK4MSS4EBkEzowk40A+JyIQ48hsBFE2pmZ0xehyyWtlH+dMChNvUph4k8LEmxQm3qQw8SaFiTcpTLxJYeJNChNvUph4k8LEmxQm3qQw8SbF2+JHjwKjxng7CS/h1dq9/zPnTx7xdg7ewou1+4R4AMHCYUkTh8LClMc/8HY+RuLF2n1F/PkmUA9gdwC6ejsfI/Fi7b4i3vGlm8VmxO15fAYv1u5b4gv6TzateINr9754hldg4k0KE29SmHiTwsSbFCbepDDxJoWJNylMvElh4k0KE29SmHiTwsSblP8PkTNKLTwuhdUAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-15"/></p>
+
+<p>The \(\chi^2\) error level of 3.3% as well as the plot suggest that the SFO model
+fits very well.  The error level at which the \(\chi^2\) test passes is slightly
+lower for the FOMC model. However, the difference appears negligible.</p>
+
+<pre><code class="r">summary(mm.L4[[&quot;SFO&quot;, 1]], data = FALSE)
+</code></pre>
+
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 17:50:09 2018 
-## Date of summary: Sun Jan 14 17:50:09 2018 
+## Date of fit:     Thu Mar  1 14:24:59 2018 
+## Date of summary: Thu Mar  1 14:24:59 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.094 s
+## Fitted with method Port using 46 model solutions performed in 0.098 s
 ## 
 ## Weighting: none
 ## 
@@ -749,19 +863,23 @@ plot(mm.L4)</code></pre>
 ## 
 ## Estimated disappearance times:
 ##        DT50 DT90
-## parent  106  352</code></pre>
-<pre class="r"><code>summary(mm.L4[[&quot;FOMC&quot;, 1]], data = FALSE)</code></pre>
-<pre><code>## mkin version:    0.9.47.1 
+## parent  106  352
+</code></pre>
+
+<pre><code class="r">summary(mm.L4[[&quot;FOMC&quot;, 1]], data = FALSE)
+</code></pre>
+
+<pre><code>## mkin version:    0.9.46.3 
 ## R version:       3.4.3 
-## Date of fit:     Sun Jan 14 17:50:09 2018 
-## Date of summary: Sun Jan 14 17:50:09 2018 
+## Date of fit:     Thu Mar  1 14:24:59 2018 
+## Date of summary: Thu Mar  1 14:24:59 2018 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 66 model solutions performed in 0.139 s
+## Fitted with method Port using 66 model solutions performed in 0.134 s
 ## 
 ## Weighting: none
 ## 
@@ -810,37 +928,11 @@ plot(mm.L4)</code></pre>
 ## 
 ## Estimated disappearance times:
 ##         DT50 DT90 DT50back
-## parent 108.9 1644    494.9</code></pre>
-</div>
-<div id="references" class="section level1 unnumbered">
-<h1>References</h1>
-<div id="refs" class="references">
-<div id="ref-ranke2014">
-<p>Ranke, Johannes. 2014. “Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0.” Umweltbundesamt Projektnummer 27452.</p>
-</div>
-</div>
-</div>
-
-
-
-</div>
-</div>
-
-</div>
-
-<script>
-
-// add bootstrap table styles to pandoc tables
-function bootstrapStylePandocTables() {
-  $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
-}
-$(document).ready(function () {
-  bootstrapStylePandocTables();
-});
-
-
-</script>
+## parent 108.9 1644    494.9
+</code></pre>
 
+<h1>References</h1>
 
 </body>
+
 </html>
diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd
index e97876da..b16dfea6 100644
--- a/vignettes/compiled_models.Rmd
+++ b/vignettes/compiled_models.Rmd
@@ -92,10 +92,10 @@ Here we get a performance benefit of a factor of
 `r factor_FOMC_SFO`

 using the version of the differential equation model compiled from C code!

 

-This vignette was built with mkin `r packageVersion("mkin")` on

+This vignette was built with mkin `r utils::packageVersion("mkin")` on

 

 ```{r sessionInfo, echo = FALSE}

-cat(capture.output(sessionInfo())[1:3], sep = "\n")

+cat(utils::capture.output(utils::sessionInfo())[1:3], sep = "\n")

 if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {

   cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))

 }