Static documentation rebuilt by staticdocs::build_site()

author: Johannes Ranke <jranke@uni-bremen.de> 2016-06-28 08:23:38 +0200
committer: Johannes Ranke <jranke@uni-bremen.de> 2016-06-28 08:23:38 +0200
commit: 7faf98ac5475bb2041d7e434478c58c2f2cec0fd (patch)
tree: 837a519b7fe4ad085a412cbb2e61d64605d8cfca /vignettes
parent: cb338bea13b3b834bc3b09e6b1014959195f37bb (diff)
5 files changed, 59 insertions, 75 deletions
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index f3eb6a0c..c7e2047f 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -124,13 +124,8 @@ $(document).ready(function () {
 
 
 <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>
-<pre class="r"><code>library(&quot;mkin&quot;)</code></pre>
-<pre><code>## Loading required package: minpack.lm</code></pre>
-<pre><code>## Loading required package: rootSolve</code></pre>
-<pre><code>## Loading required package: inline</code></pre>
-<pre><code>## Loading required package: methods</code></pre>
-<pre><code>## Loading required package: parallel</code></pre>
-<pre class="r"><code>print(FOCUS_2006_D)</code></pre>
+<pre class="r"><code>library(&quot;mkin&quot;)
+print(FOCUS_2006_D)</code></pre>
 <pre><code>##      name time  value
 ## 1  parent    0  99.46
 ## 2  parent    0 102.04
@@ -195,10 +190,10 @@ $(document).ready(function () {
 <p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAGACAMAAABFpiBcAAAC4lBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NVVVVWVlZXV1dYWFhZWVlaWlpcXFxdXV1eXl5fX19gYGBhYWFiYmJkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t9fX1+fn5/f3+AgICCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKUlJSVlZWWlpaXl5eYmJiZmZmbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7///97HrBkAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAUIElEQVR4nO3df1xVdZ7H8S+K8uuCOnoLf7b+aHBWRRHB1FXLGjN/ZK2rW8msOrvNpjllgxNTq+2AO5Xjj1lkCgILxcRwG39Mo4Szmr82kbiuQutorokhIW4Jwf3+v99z7gXOvZx776HzPYcPPN7PPw73nss9n3Ovr7jcL5iMAxDGDH5e/XM/BZBmTb3kQMuH7gCQZmi57EATDX4igAGJCBQoQ6BAGgIF0hAokIZAgTQECqQhUCANgQJpCBRIQ6BAGgIF0hAokIZAgTQECqQhUCANgQJpCBRIQ6BAGgIF0hAokIZAgTQECqQhUCANgQJpCBRIQ6BAGgIF0hAokIZAgTQECqQhUCANgQJpCBRIQ6BAGuVAmUt7rTglbuYn0o4N3US3CfRI+PritJgqaQcHW528EPJTDtzU29ttAl20lnP33JelHRxstXaz9lpFkWqPT5JzD+nd0f5AXezkjNgJO8WlTx8dGDG+SDn20QdTecuOxKjRm93KtfnxI/L4VcbYq233ckecEtu8yQbPAojxDXTO7CWKhNe1O+kEmlBaWxhbwlsGj83+YGX4bXHsCblVPMuReXiL8w1xbUqle1uvuubrrKyh7V63mPJfW9kQg2cBxPgG+tAR9cNL/6bdSSfQErHNmsW/ThdfFG8rr+Msh/MWR7bYvX+auJbPeaO6W/MSX8WalIP3MXgWQEy3CrRObI8PEptLezJS1BLPcn6ZqZzi2jnuiVMbaB37SmzL7jV4FkDMgklLNO7xvsQnanfG5+ndsSsCVV6sS/tx/mL8itzy1hJrWdF1hbdL/0DdkSJiXpBk8CyAmJVpRRrjX1E/LHpau3PSfr07dkWguWK7ejqvDbumfOFsLXF4utiUZAQIlC9aLzaL0w2eBRDTrV7inZv2r2bF/E7f107sntRr+7eeEt8KzziQ2f89TaC9s6+13+1oxJvH1jmqDZ4FEOMX6M5qxSqigZ6e4RhXIC7tGhk7+/j6uMueJN35E6MScrgm0DXRmzT3K06OnXna4EkANb6B/tMoj0LtTjqBGjwS9CDp/x7yUx7/WG8vAgU7NDWH/JS7untpB1rIWtUZvxP0JJR/Fg+AQIE2BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIE0BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIE0BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIE0BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIE0BAqkSQ+0MipZx5g+0XYJj7JrUqR9D6q3bZMi+to2KlyvFH9RlZID5eVndGxNLLDL8F/bNekf/8auSQVx2+2atGy+XZPyw/RK8feZ0e7M/eMxh+aauntnTDhv16R3lts1id9zw65Jm35h1yR3mNTDIdAOEKgpCNRqCNQUBGo1BGoKArUaAjUFgVoNgZqCQK2GQE1BoFZDoKaQCtS1UdJphPbCl3ZNOr7Nrkl85Td2TfrwPbsm8aelHg3/yjuQhkCBNAQKpCFQIA2BAmkIFEhDoEAaAgXSECiQhkCBNAQKpCFQIA2BAmmmAi1OiZv5iawzCcSdnxh9/6Zme8ZVx7m4LZPO/bjffa+7bRj15U/iBz5zk1s/KVV95tqnSBpnJtAj4euL02KqzJ9EUHvZmoOb+2XaMq4xhSlPs/WTLvT7WfErfd62flTLA6Pe2ZM8j1s9qWmr+sy1T5E1zkygi9aKr29zXzZ9DsHNUn4pOneULePWTVSfZusnPfus2OT/1vpRLnaI8yr2tcWTsiOYJ9C2KbLGmQjUHXFKbPMmmz6H4GbvEJtShx3jDjguKE+zDZMGnvF8tHyUi/0n5zdYrcWTaioq1EDbpkgbZyLQW0z51qZsiOlzCK152QIbxl1z5nPlabZ+0v+xvQ84fvhmi/Wj3HOmnf7siUdteFBqoG1TpI0zEWgVaxLb8j6mzyGkvyyMr7Z+XPNDaZ6n2foH9jn7YUnV+4O32DDqv6MZ63XRhgelBto2Rdo4E4HWsa/Etuxe0+cQQtPGyGU3bBiXldDgeZqtf2A32EmxzU+wftTJuF/duJk21YYHpQbaNkXaODPfg0aeFduCJNPnEFzD9Pv+bMu4x5kq2YYH1ty7XmxPxVg/atVYsbmpfA9q9STP96CtU6SNM/Uufr3YLE43fQ7BrRpba8+4W9cFVnbTjgf24C6xyUq2ftQvhzRyfpHVWz/J+y6+dYqscWYCPRrx5rF1jmrT5xDU3cgncxQ2jVOfZusnfRD7r/+R0fd960e5Yh7eU5SkrINaPckTaNsUWePM/SQpOXbmadOnENwFzwsvs2mc52m2flJBUkzSHjtGnXls0ICnamyYxLw/SWqdImkcfhYPpCFQIA2BAmkIFEhDoEAaAgXSECiQZjTQ+ud+CiDNmnrJgZYP3QEgzVB7/jHZz6z+dXrooWz6145t/H/UQ4+CQIE0BAqk2RTokfkG7w7gw6ZAW24ZvDuAD5sCBfh+ECiQZlOgWAeF7wfv4oE0BAqkIVAgDeugQBrWQYE0LDMBaQgUSMM6KJCGd/FAGgIF0hAokIZ1UCAN66BAGpaZgDQECqRhHRRIw7t4IA2BAmkIFEjDOiiQhnVQIA3LTEAaAgXS7F8HdVn4PxO38tjQJex/F28iIu8/ohd4JwLtcbp/oEu/aL+MQI35+FpXn4GOw1/p7e3+gWohUGOWFZo8QPVHHjeknI7H3EN6e+1fB1Uiujjsme80O07OiJ2wU1z69NGBEeOLlGFHH0zlLTsSo0ZvdivX5sePyONXGWOvth+yYt6AuEdcnmq9n9Hx2KDLdKBPTnxYMfrV0J9qWJcGqlkHFRFVxq9s4ZodCaW1hbElvGXw2OwPVobfFsMm5FbxLEfm4S3ON8S1KZXubb3qmq+zsoa2ezUPeWpX4bxUb6Cez+h4bNBlOtDF+9QPG35l/lzadGmgGi523rm8xWdHidhmzeJfp5/i/LYaXI5o2pEtdu+fJq7lc97IXL4v8VfZJc5r3vYG6v2MDscGXQg0CBcb6JzmG2id2B4fJDaX9mSkqMGd5fwyUznFtXPc26Em0Jal/dPy6ltv8H5Gh2ODrqkPLDFn6HT1w7i/Nnkcrfh39E61K9ZBt58I26a5zcVuim1pP85fjF+RW95aYi0ruq7wdukfKOefb3xkwG80N6iB+h0bdD32fJE5KS+pH5b+rcnjaE3ar3eqXfMu/nnHFa7ZkSu2q6fz2rBryhfO1uCGp4tNSUaAQG+tFW+fPnT4B+p3bNCFl3h/foE2jFjo1uxwbtq/mhXzO31fO7F7Uq/t33qCeys840Bm//c0gfbObl+/ax60oqhofmrHQH2ODbrMB7rzluLlnhooP8h2a3acnuEYVyAu7RoZO/v4+rjLnuDc+ROjEnK4JtA10ZvaD3lsakz/BVUdA/U5NugyHehzAzx+J+V0PKisg3aA1XXbLS/u6jPQsbBUby+B3wdFoLZroLjScVt3b5f9ul0ha3WiE4G236vO+J2gG8PvgwJp+HvxQBr+VieQhkCBNAQKpBFYBwUIjMA6KEBgWGYC0hAokIZ1UCAN7+KBNAQKpCFQIA3roEAa1kGBNCwzAWkIFEjDOiiQhnfxQBoCBdIQKJCGdVAgDeugQBqWmYA0BAqkYR0USMO7eCANgQJpCBRIwzookIZ1UCANy0xAGgIF0rAOCqThXTyQhkCBNAQKpGEdFEjDOiiQhmUmIA2BAmlYBwXS8C4eSEOgQBoCBdKwDgqkYR0USMMyE5CGQIE0rIMCaXgXD6TZH6jL6JEAulmgzBXkxtRgN0J3Zf86qDWBNm0NWi/IUeCWfUR3fvDb7V8HtSTQ7AiGQG0Qezv055wpUr1fb+iId2KC327/MpMS6MVhz3yn2XFyRuyEneLSp48OjBhfpAw7+mAqb9mRGDV6s1u5Nj9+RB6/yhh7VXNC++bcM2Z3QeKAkfs4r6moQKA2MBLohB8vUfxViC+NXiQDrYxf2aLdkVBaWxhbwlsGj83+YGW4eA7YhNwqnuXIPLzF+Ya4NqXSva1XXfN1VtagOaFpV5rXsXk1zT+P91xHoNYzEuj4CvVDWp6hIxIJVLMO6mLnncs1fYodJWKbNYt/nX6K89tKaCxHfFfgyBa7908T18R/i43qbm2D7E9iGLuibDzXEaj1emygPu/iBzqn+QZaJ7bHB4nNpT0ZKWqJZzm/zFROce0c9/TnG6jL+/2sC4Hapu8TS0Lq532JTwn9qcITfYJP7IpAt58I26a5zcVuim1pP85fjF+RW95aYi0ruq7wpodAKYh+pyik4W+oH2b/c+hPFQoig0/smnXQ5x1XuGZHrtiuns5rw64pXzhbSxyeLjYlGQiUkB77Eu+3DtowYqFbs8O5af9qVszv9H3txO5JvbZ/64ntrfCMA5n939ME2jv7muaEEKj9DAV6sFrxZLcK1H8d9CDbrdlxeoZjXIG4tGtk7Ozj6+Mue2Jz50+MSsjhmkDXRG/SnBACtZ+RQJeM8vijoSMSCTQY/HC++xj8jewj3r03+O0IFDrhru2H7LLfBy1krU50ItD2e9UZvxN0Y/h9UCANgQJpCBRIw9+LB9Lw9+KBNALLTACBIVAgDX8vHkjDu3ggDYECaQgUSMM6KJCGdVAgDctMQBoCBdKwDgqk4V08kIZAgTQECqRhHRRIwzookIZlJiANgQJpWAcF0vAuHkhDoEAaAgXSsA4KpGEdFEjDMhOQhkCBNKyDAml4Fw+kIVAgDYECaVgHBdKwDgqkYZkJSEOgQBrWQYE0vIsH0hAokIZAgTSsgwJpWAcF0rDMBKQhUCAN66BAGt7FA2kIFEhDoEAa1kGh01xGY5BwEKyDQqeFbivVpbubaXbTCtSg/zka5MZD10wcGSQK1VbTVqYf6NIvjB/Ei1agBU97L1QVeVzS3Lh4n4kjg0Qh2sqOYAEC7cRBWtFaB20L9GeTlyimrNLciECpUNq6OOyZ79r3sH1z7hmzuyBxwEjxh1RTUeEXaMW8AXGPuDwv8ezo/PgReXoH0UXrXXxboM/+Tv2Qs1JzIwKlQrRVGb+yRbOHTbvSvI7Nq2n+ebznuk+gzUOe2lU4L9Ub6JRK97ZedToH0YVAodNc7LxzuU9a7E/iT55dUTae6z6BXmXiW7Wat72B5nPeKC50PIguWoG+Nvhhj2Fj1Q8/GvpwO+c2g6cAFnOxgc5pvoG6vN9UuvQCbVnaPy2v3rubnfNc6HgQXbTWQX8/5yOPhWvUDy/M+6jdjCKDpwAWc7HtJ8J8vlwED5Tzzzc+MuA33kBd3Buo/0F00VoHxUt8t6BU+LzjimZP8EBvrXVz/qHDP1D/g+giusz07IZqRSYCpUhpq2HEQnf7nuCBNg9aUVQ0P7VjoL4H0UU00NdHeWRqbkSgVKgVHmS72/eEeIk/NjWm/4KqjoH6HkQXrXXQvauC3Pj3hw2eAvQgtN7Fu5uC3Nho8AygJ6EVKHQnhaxV3fe42SAECqTRWgcF8ENrHRTAD61lJgA/CBRIo7UOCuAH7+KBNAQKpCFQIA3roEAa1kGBNCwzAWkIFEjDOiiQhnfxQBoCBdIQKJCGdVAgDeugQBqWmYA0BAqkYR0USMO7eCANgQJpCBRIwzookIZ1UCANy0xAGgIF0rAOCqThXTyQhkCBNAQKpEkPtDIqWcfQsGiLhUdZPCCyj8UDesJD6PMjvT99M6IqJQfKy8/o2DKuwGKj/8XiAesmWjygYOQGiwe8kGTxgIKR7+r96ZvxmdHuDAeqy/qX+KmfWDzgD49ZPIBPOW3xgP0LLR7Ak/7L6gkBIVAEGhoCDQiBhoZAA0KgBiBQMxAoAg0NgQaEQENDoAEhUAMQqBkIFIGG1m0DdW2UdBoB/fKKxQMqf23xAJ7+hcUDzmdZPIC/9L9WTwjIXKAAFkOgQBoCBdIQKJCGQIE0BAqkIVAgDYECaQgUSEOgQBoCBdIQKJCGQIG0zgdanBI385MOV3z2mqM/gPNUl5zjB5jgzk+Mvn9Ts3UDqh93/uDvZP1aUKAnqTpO0rOk/xCYIl3OBGM6HeiR8PXFaTFVfld89pqjP4A3bWWyAtWfsJetObi5X6ZlAxrvT9pTnDxHxvEDPkm8MUXSs6Q/4DDLzsnJOSllgkGdDnTRWvHFZu7Lfld89pqjPyA7gkkLVH/CLOWXr3NHWTbgj+wi5xdYvYwBAR4C5+smSnqW9AdsHyrl4J3R2UDdEafENm+y7xWfveboD+A1FRWyAg0wYfYOcaHUYdmAQ0vcnNezryQMCPQQ+AHHBTnPUoABL8zm9W4Zxzeus4HeYjfFtmyI7xWfveboD1BPVVKggSfw5mULrBzw5ZnlD0k4fsAJ15z5kp6lAAMWjRnB4n7RKGOCUZ0NtIo1iW15H98rPnvN0R+gnqqkQANP+MvC+GorB8xhvSskHD/QhOaH0mQ9SwEewuSJJxuODN8gY4JRnQ20Tn2NKrvX94rPXnP0B6inKinQQBOaNkYuu2HlAM5r10j5HjfAhKyEBlnPUuCHwPm7Y2RMMKrT34NGnhXbgiTfKz57zdEfoJ6qrO9B9Sc0TL/vz1YO+PgwV74H/dK6CY+ri0As2bIBHqdjJAwwrPPv4teLzeJ0vys+e83RH8DlBRpgwqqxtZKOrz/glWHie7fKsDvWTbh1XWBlNy0bUBPxrriwJUXGAKM6HejRiDePrXOIb9WWfqG50nbBPP0BXGKguhPuRj6Zo7BswIXIBXt3T1ws4/hBniRZz5L+gJ/0++0fNkQXS5lg0Pf4SVJy7Ezlf0WgPhOtV9ovmKc/QGKguhMueF4f5fzoV/chlM6Ic/6DrK/SgZ4kac+S7oBvXhwWlbRHzgCD8LN4IA2BAmkIFEhDoEAaAgXSECiQ9v8VYkypuE0lagAAAABJRU5ErkJggg==" alt /><!-- --></p>
 <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p>
 <pre class="r"><code>summary(fit)</code></pre>
-<pre><code>## mkin version:    0.9.43 
+<pre><code>## mkin version:    0.9.43.9000 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 07:48:50 2016 
-## Date of summary: Tue Jun 28 07:48:50 2016 
+## Date of fit:     Tue Jun 28 08:19:31 2016 
+## Date of summary: Tue Jun 28 08:19:31 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
@@ -206,7 +201,7 @@ $(document).ready(function () {
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted with method Port using 153 model solutions performed in 1.659 s
+## Fitted with method Port using 153 model solutions performed in 1.706 s
 ## 
 ## Weighting: none
 ## 
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 8435ce23..05b9bdbd 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -233,17 +233,17 @@ FOCUS_2006_L1_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L1)</code></pre>
 <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:    0.9.43 
+<pre><code>## mkin version:    0.9.43.9000 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 07:38:06 2016 
-## Date of summary: Tue Jun 28 07:38:06 2016 
+## Date of fit:     Tue Jun 28 08:19:32 2016 
+## Date of summary: Tue Jun 28 08:19:32 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.235 s
+## Fitted with method Port using 37 model solutions performed in 0.245 s
 ## 
 ## Weighting: none
 ## 
@@ -326,10 +326,10 @@ summary(m.L1.SFO)</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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" alt /><!-- --></p>
 <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre>
-<pre><code>## mkin version:    0.9.43 
+<pre><code>## mkin version:    0.9.43.9000 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 07:38:07 2016 
-## Date of summary: Tue Jun 28 07:38:07 2016 
+## Date of fit:     Tue Jun 28 08:19:34 2016 
+## Date of summary: Tue Jun 28 08:19:34 2016 
 ## 
 ## 
 ## Warning: Optimisation by method Port did not converge.
@@ -341,7 +341,7 @@ summary(m.L1.SFO)</code></pre>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 188 model solutions performed in 1.119 s
+## Fitted with method Port using 188 model solutions performed in 1.216 s
 ## 
 ## Weighting: none
 ## 
@@ -423,17 +423,17 @@ plot(m.L2.FOMC, show_residuals = TRUE,
      main = &quot;FOCUS L2 - FOMC&quot;)</code></pre>
 <p><img 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" alt /><!-- --></p>
 <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
-<pre><code>## mkin version:    0.9.43 
+<pre><code>## mkin version:    0.9.43.9000 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 07:38:09 2016 
-## Date of summary: Tue Jun 28 07:38:09 2016 
+## Date of fit:     Tue Jun 28 08:19:36 2016 
+## Date of summary: Tue Jun 28 08:19:36 2016 
 ## 
 ## Equations:
 ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 81 model solutions performed in 0.492 s
+## Fitted with method Port using 81 model solutions performed in 0.537 s
 ## 
 ## Weighting: none
 ## 
@@ -493,10 +493,10 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
      main = &quot;FOCUS L2 - DFOP&quot;)</code></pre>
 <p><img 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" alt /><!-- --></p>
 <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
-<pre><code>## mkin version:    0.9.43 
+<pre><code>## mkin version:    0.9.43.9000 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 07:38:11 2016 
-## Date of summary: Tue Jun 28 07:38:11 2016 
+## Date of fit:     Tue Jun 28 08:19:39 2016 
+## Date of summary: Tue Jun 28 08:19:39 2016 
 ## 
 ## Equations:
 ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -505,7 +505,7 @@ 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 2.04 s
+## Fitted with method Port using 336 model solutions performed in 2.267 s
 ## 
 ## Weighting: none
 ## 
@@ -582,10 +582,10 @@ plot(mm.L3)</code></pre>
 <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:    0.9.43 
+<pre><code>## mkin version:    0.9.43.9000 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 07:38:13 2016 
-## Date of summary: Tue Jun 28 07:38:13 2016 
+## Date of fit:     Tue Jun 28 08:19:41 2016 
+## Date of summary: Tue Jun 28 08:19:42 2016 
 ## 
 ## Equations:
 ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -594,7 +594,7 @@ plot(mm.L3)</code></pre>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 137 model solutions performed in 0.846 s
+## Fitted with method Port using 137 model solutions performed in 0.924 s
 ## 
 ## Weighting: none
 ## 
@@ -682,17 +682,17 @@ plot(mm.L4)</code></pre>
 <p><img 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" alt /><!-- --></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:    0.9.43 
+<pre><code>## mkin version:    0.9.43.9000 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 07:38:14 2016 
-## Date of summary: Tue Jun 28 07:38:14 2016 
+## Date of fit:     Tue Jun 28 08:19:42 2016 
+## Date of summary: Tue Jun 28 08:19:43 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.283 s
+## Fitted with method Port using 46 model solutions performed in 0.307 s
 ## 
 ## Weighting: none
 ## 
@@ -742,17 +742,17 @@ plot(mm.L4)</code></pre>
 ##        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:    0.9.43 
+<pre><code>## mkin version:    0.9.43.9000 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 07:38:14 2016 
-## Date of summary: Tue Jun 28 07:38:15 2016 
+## Date of fit:     Tue Jun 28 08:19:43 2016 
+## Date of summary: Tue Jun 28 08:19:43 2016 
 ## 
 ## Equations:
 ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 66 model solutions performed in 0.383 s
+## Fitted with method Port using 66 model solutions performed in 0.414 s
 ## 
 ## Weighting: none
 ## 
diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf
index efda950f..1f9560b0 100644
--- a/vignettes/FOCUS_Z.pdf
+++ b/vignettes/FOCUS_Z.pdf
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index c3ffb035..cec76ef9 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -226,13 +226,8 @@ div.tocify {
 <pre><code>##            gcc 
 ## &quot;/usr/bin/gcc&quot;</code></pre>
 <p>First, we build a simple degradation model for a parent compound with one metabolite.</p>
-<pre class="r"><code>library(&quot;mkin&quot;)</code></pre>
-<pre><code>## Loading required package: minpack.lm</code></pre>
-<pre><code>## Loading required package: rootSolve</code></pre>
-<pre><code>## Loading required package: inline</code></pre>
-<pre><code>## Loading required package: methods</code></pre>
-<pre><code>## Loading required package: parallel</code></pre>
-<pre class="r"><code>SFO_SFO &lt;- mkinmod(
+<pre class="r"><code>library(&quot;mkin&quot;)
+SFO_SFO &lt;- mkinmod(
   parent = mkinsub(&quot;SFO&quot;, &quot;m1&quot;),
   m1 = mkinsub(&quot;SFO&quot;))</code></pre>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
@@ -255,22 +250,22 @@ mb.1 &lt;- microbenchmark(
 print(mb.1)</code></pre>
 <pre><code>## Unit: seconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 13.694897 13.774112 13.820936 13.853327 13.883956
-##       Eigenvalue based  2.087861  2.089503  2.116323  2.091145  2.130555
-##      deSolve, compiled  1.794975  1.799892  1.814653  1.804808  1.824492
-##        max neval cld
-##  13.914585     3   c
-##   2.169964     3  b 
-##   1.844177     3 a</code></pre>
+##  deSolve, not compiled 25.422123 25.889685 26.065978 26.357247 26.387905
+##       Eigenvalue based  2.243667  2.254539  2.277770  2.265412  2.294821
+##      deSolve, compiled  1.849468  1.865343  1.871339  1.881219  1.882274
+##       max neval cld
+##  26.41856     3   b
+##   2.32423     3  a 
+##   1.88333     3  a</code></pre>
 <pre class="r"><code>autoplot(mb.1)</code></pre>
-<p><img src="data:image/png;base64,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" title alt width="672" /></p>
-<p>We see that using the compiled model is by a factor of 7.7 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>
+<p><img 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" title alt width="672" /></p>
+<p>We see that using the compiled model is by a factor of 14 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 class="r"><code>rownames(smb.1) &lt;- smb.1$expr
 smb.1[&quot;median&quot;]/smb.1[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
-<pre><code>##                         median
-## deSolve, not compiled 7.675788
-## Eigenvalue based      1.158652
-## deSolve, compiled     1.000000</code></pre>
+<pre><code>##                          median
+## deSolve, not compiled 14.010730
+## Eigenvalue based       1.204226
+## deSolve, compiled      1.000000</code></pre>
 </div>
 <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level2">
 <h2>Benchmark for a model that can not be solved with Eigenvalues</h2>
@@ -290,20 +285,20 @@ smb.1[&quot;median&quot;]/smb.1[&quot;deSolve, compiled&quot;, &quot;median&quot
 <pre class="r"><code>smb.2 &lt;- summary(mb.2)
 print(mb.2)</code></pre>
 <pre><code>## Unit: seconds
-##                   expr       min        lq      mean   median        uq
-##  deSolve, not compiled 29.120048 29.170013 29.246607 29.21998 29.309886
-##      deSolve, compiled  3.338458  3.343954  3.379437  3.34945  3.399926
+##                   expr       min       lq      mean    median        uq
+##  deSolve, not compiled 54.386189 54.39423 54.477986 54.402271 54.523884
+##      deSolve, compiled  3.424205  3.53522  3.574587  3.646236  3.649778
 ##        max neval cld
-##  29.399796     3   b
-##   3.450402     3  a</code></pre>
+##  54.645498     3   b
+##   3.653319     3  a</code></pre>
 <pre class="r"><code>smb.2[&quot;median&quot;]/smb.2[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
 <pre><code>##   median
 ## 1     NA
 ## 2     NA</code></pre>
 <pre class="r"><code>autoplot(mb.2)</code></pre>
-<p><img 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" title alt width="672" /></p>
-<p>Here we get a performance benefit of a factor of 8.7 using the version of the differential equation model compiled from C code!</p>
-<p>This vignette was built with mkin 0.9.43 on</p>
+<p><img 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" title alt width="672" /></p>
+<p>Here we get a performance benefit of a factor of 14.9 using the version of the differential equation model compiled from C code!</p>
+<p>This vignette was built with mkin 0.9.43.9000 on</p>
 <pre><code>## R version 3.3.1 (2016-06-21)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
 ## Running under: Debian GNU/Linux 8 (jessie)</code></pre>
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index 54605dfc..f9704eda 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -236,14 +236,8 @@ div.tocify {
 <div id="abstract" class="section level1">
 <h1>Abstract</h1>
 <p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> <span class="citation">(Ranke 2016)</span> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p>
-<pre class="r"><code>require(mkin)</code></pre>
-<pre><code>## Loading required package: mkin</code></pre>
-<pre><code>## Loading required package: minpack.lm</code></pre>
-<pre><code>## Loading required package: rootSolve</code></pre>
-<pre><code>## Loading required package: inline</code></pre>
-<pre><code>## Loading required package: methods</code></pre>
-<pre><code>## Loading required package: parallel</code></pre>
-<pre class="r"><code>m_SFO_SFO_SFO &lt;- mkinmod(parent = mkinsub(&quot;SFO&quot;, &quot;M1&quot;),
+<pre class="r"><code>require(mkin)
+m_SFO_SFO_SFO &lt;- mkinmod(parent = mkinsub(&quot;SFO&quot;, &quot;M1&quot;),
                          M1 = mkinsub(&quot;SFO&quot;, &quot;M2&quot;),
                          M2 = mkinsub(&quot;SFO&quot;), 
                          use_of_ff = &quot;max&quot;, quiet = TRUE)
author	Johannes Ranke <jranke@uni-bremen.de>	2016-06-28 08:23:38 +0200
committer	Johannes Ranke <jranke@uni-bremen.de>	2016-06-28 08:23:38 +0200
commit	7faf98ac5475bb2041d7e434478c58c2f2cec0fd (patch)
tree	837a519b7fe4ad085a412cbb2e61d64605d8cfca /vignettes
parent	cb338bea13b3b834bc3b09e6b1014959195f37bb (diff)