aboutsummaryrefslogtreecommitdiff
path: root/docs/reference/ds_mixed.html
diff options
context:
space:
mode:
authorJohannes Ranke <jranke@uni-bremen.de>2023-02-13 05:19:08 +0100
committerJohannes Ranke <jranke@uni-bremen.de>2023-02-13 05:19:08 +0100
commit8d1a84ac2190538ed3bac53a303064e281595868 (patch)
treeacb894d85ab7ec87c4911c355a5264a77e08e34b /docs/reference/ds_mixed.html
parent51d63256a7b3020ee11931d61b4db97b9ded02c0 (diff)
parent4200e566ad2600f56bc3987669aeab88582139eb (diff)
Merge branch 'main' into custom_lsoda_call
Diffstat (limited to 'docs/reference/ds_mixed.html')
-rw-r--r--docs/reference/ds_mixed.html240
1 files changed, 240 insertions, 0 deletions
diff --git a/docs/reference/ds_mixed.html b/docs/reference/ds_mixed.html
new file mode 100644
index 00000000..64b02749
--- /dev/null
+++ b/docs/reference/ds_mixed.html
@@ -0,0 +1,240 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Synthetic data for hierarchical kinetic degradation models — ds_mixed • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Synthetic data for hierarchical kinetic degradation models — ds_mixed"><meta property="og:description" content="The R code used to create this data object is installed with this package in
+the 'dataset_generation' directory."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></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]--></head><body data-spy="scroll" data-target="#toc">
+
+
+ <div class="container template-reference-topic">
+ <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
+ <div class="container">
+ <div class="navbar-header">
+ <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+ <span class="sr-only">Toggle navigation</span>
+ <span class="icon-bar"></span>
+ <span class="icon-bar"></span>
+ <span class="icon-bar"></span>
+ </button>
+ <span class="navbar-brand">
+ <a class="navbar-link" href="../index.html">mkin</a>
+ <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span>
+ </span>
+ </div>
+
+ <div id="navbar" class="navbar-collapse collapse">
+ <ul class="nav navbar-nav"><li>
+ <a href="../reference/index.html">Functions and data</a>
+</li>
+<li class="dropdown">
+ <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+ Articles
+
+ <span class="caret"></span>
+ </a>
+ <ul class="dropdown-menu" role="menu"><li>
+ <a href="../articles/mkin.html">Introduction to mkin</a>
+ </li>
+ <li>
+ <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
+ </li>
+ <li>
+ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
+ </li>
+ <li>
+ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a>
+ </li>
+ <li>
+ <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
+ </li>
+ <li>
+ <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
+ </li>
+ <li>
+ <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
+ </li>
+ <li>
+ <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
+ </li>
+ <li>
+ <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
+ </li>
+ <li>
+ <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
+ </li>
+ <li>
+ <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
+ </li>
+ </ul></li>
+<li>
+ <a href="../news/index.html">News</a>
+</li>
+ </ul><ul class="nav navbar-nav navbar-right"><li>
+ <a href="https://github.com/jranke/mkin/" class="external-link">
+ <span class="fab fa-github fa-lg"></span>
+
+ </a>
+</li>
+ </ul></div><!--/.nav-collapse -->
+ </div><!--/.container -->
+</div><!--/.navbar -->
+
+
+
+ </header><div class="row">
+ <div class="col-md-9 contents">
+ <div class="page-header">
+ <h1>Synthetic data for hierarchical kinetic degradation models</h1>
+ <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ds_mixed.R" class="external-link"><code>R/ds_mixed.R</code></a></small>
+ <div class="hidden name"><code>ds_mixed.Rd</code></div>
+ </div>
+
+ <div class="ref-description">
+ <p>The R code used to create this data object is installed with this package in
+the 'dataset_generation' directory.</p>
+ </div>
+
+
+
+ <div id="ref-examples">
+ <h2>Examples</h2>
+ <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span> <span class="va">sfo_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></span>
+<span class="r-in"><span> <span class="va">sfo_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">sfo_mmkin</span>, no_random_effect <span class="op">=</span> <span class="st">"parent_0"</span><span class="op">)</span></span></span>
+<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">sfo_saem</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="ds_mixed-1.png" alt="" width="700" height="433"></span>
+<span class="r-in"><span><span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># This is the code used to generate the datasets</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/readLines.html" class="external-link">readLines</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span><span class="st">"dataset_generation/ds_mixed.R"</span>, package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span><span class="op">)</span>, sep <span class="op">=</span> <span class="st">"\n"</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> # Synthetic data for hierarchical kinetic models</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> # Refactored version of the code previously in tests/testthat/setup_script.R</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> # is always 15 for consistency</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> library(mkin) # We use mkinmod and mkinpredict</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> n &lt;- 15</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> log_sd &lt;- 0.3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> err_1 = list(const = 1, prop = 0.05)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tc &lt;- function(value) sigma_twocomp(value, err_1$const, err_1$prop)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> const &lt;- function(value) 2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> SFO &lt;- mkinmod(parent = mkinsub("SFO"))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sfo_pop &lt;- list(parent_0 = 100, k_parent = 0.03)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sfo_parms &lt;- as.matrix(data.frame(</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd)))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_sfo &lt;- lapply(1:n, function(i) {</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_mean &lt;- mkinpredict(SFO, sfo_parms[i, ],</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> c(parent = sfo_pop$parent_0), sampling_times)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> add_err(ds_mean, tc, n = 1)[[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_sfo, "pop") &lt;- sfo_pop</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_sfo, "parms") &lt;- sfo_parms</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> FOMC &lt;- mkinmod(parent = mkinsub("FOMC"))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> fomc_pop &lt;- list(parent_0 = 100, alpha = 2, beta = 8)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> fomc_parms &lt;- as.matrix(data.frame(</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> beta = rlnorm(n, log(fomc_pop$beta), 0.2)))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_fomc &lt;- lapply(1:n, function(i) {</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_mean &lt;- mkinpredict(FOMC, fomc_parms[i, ],</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> c(parent = fomc_pop$parent_0), sampling_times)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> add_err(ds_mean, tc, n = 1)[[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_fomc, "pop") &lt;- fomc_pop</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_fomc, "parms") &lt;- fomc_parms</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP &lt;- mkinmod(parent = mkinsub("DFOP"))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> dfop_pop &lt;- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> dfop_parms &lt;- as.matrix(data.frame(</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k1 = rlnorm(n, log(dfop_pop$k1), log_sd),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k2 = rlnorm(n, log(dfop_pop$k2), log_sd),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_dfop &lt;- lapply(1:n, function(i) {</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_mean &lt;- mkinpredict(DFOP, dfop_parms[i, ],</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> c(parent = dfop_pop$parent_0), sampling_times)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> add_err(ds_mean, tc, n = 1)[[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_dfop, "pop") &lt;- dfop_pop</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_dfop, "parms") &lt;- dfop_parms</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> HS &lt;- mkinmod(parent = mkinsub("HS"))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> hs_pop &lt;- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> hs_parms &lt;- as.matrix(data.frame(</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k1 = rlnorm(n, log(hs_pop$k1), log_sd),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k2 = rlnorm(n, log(hs_pop$k2), log_sd),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> tb = rlnorm(n, log(hs_pop$tb), 0.1)))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_hs &lt;- lapply(1:n, function(i) {</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_mean &lt;- mkinpredict(HS, hs_parms[i, ],</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> c(parent = hs_pop$parent_0), sampling_times)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> add_err(ds_mean, const, n = 1)[[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_hs, "pop") &lt;- hs_pop</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_hs, "parms") &lt;- hs_parms</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> DFOP_SFO &lt;- mkinmod(</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> parent = mkinsub("DFOP", "m1"),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> m1 = mkinsub("SFO"),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> quiet = TRUE)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> dfop_sfo_pop &lt;- list(parent_0 = 100,</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1 = 0.007, f_parent_to_m1 = 0.5,</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k1 = 0.1, k2 = 0.02, g = 0.5)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> dfop_sfo_parms &lt;- as.matrix(data.frame(</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> f_parent_to_m1 = plogis(rnorm(n,</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd)))</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_dfop_sfo_mean &lt;- lapply(1:n,</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> function(i) {</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ],</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> }</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> )</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> set.seed(123456)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> ds_dfop_sfo &lt;- lapply(ds_dfop_sfo_mean, function(ds) {</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> add_err(ds,</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> n = 1, secondary = "m1")[[1]]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> })</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_dfop_sfo, "pop") &lt;- dfop_sfo_pop</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> attr(ds_dfop_sfo, "parms") &lt;- dfop_sfo_parms</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)</span>
+</code></pre></div>
+ </div>
+ </div>
+ <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+ <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
+ </nav></div>
+</div>
+
+
+ <footer><div class="copyright">
+ <p></p><p>Developed by Johannes Ranke.</p>
+</div>
+
+<div class="pkgdown">
+ <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p>
+</div>
+
+ </footer></div>
+
+
+
+
+
+
+ </body></html>
+

Contact - Imprint