Improve README, introductory vignette and some other docsv1.0.3

Also bump version to 1.0.3.

1 files changed, 82 insertions, 108 deletions

diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 1c03f484..0ed46483 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -11,10 +11,22 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2020-11-19" />
 
 <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title>
 
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
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@@ -1380,6 +1353,7 @@ h6 {
 
 
 
+
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@@ -1566,7 +1540,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">2020-11-19</h4>
+<h4 class="date">Last change 17 November 2016 (rebuilt 2021-02-15)</h4>
 
 </div>
 
@@ -1585,10 +1559,10 @@ 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 used for fitting:    0.9.50.4 
+<pre><code>## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Thu Nov 19 14:46:14 2020 
-## Date of summary: Thu Nov 19 14:46:14 2020 
+## Date of fit:     Mon Feb 15 14:11:19 2021 
+## Date of summary: Mon Feb 15 14:11:19 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -1671,32 +1645,32 @@ summary(m.L1.SFO)</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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5F0sTyBEkhQq1Jf1tz8Y5l52gsYKNAbj/jayoUbVTjFlxmWJ1Dd4QBTyUmAaD/tBQwUCBIrNzjZ3qXaoEtOnOLLjPguwg+O2sakApXazbaPTAfYX0p7AUMFgvbF3Nc9vP5tuWBO8WXG7yYYnjlQ+8MPgQTymg2wjPzD/N/w0l7AYIFuWDV4CHDK/iNO8RFTIZBA020iJ5Vp0P52sudI7QUMFuhPF69yDWtVia3EKT5iKgQSKGu8S4X5b2oREvBEewGDBUpq4Lfi9F9ZD505xUdMhaD3gTJ37s3QsctggV6W3ef+DGB9OyPiI8IjVpNWgwWCCS36DYQD7r/zGx/hCfMXKDvavphTzX38hkf4wvwFAngX65nOb3SEN6QgEEDoZH6jI7whDYHuO13iNzzCF9IQCJY2xBb25olEBFK0nctvfIQnJCIQ3HK+xm8CCD9IRSCICcGhoMwRyQiU09SgNoyIiZGMQPC3021+U0D4QDoCwcy27JfYxcDSZZvr7n4UMTESEii7URzAL0VrTZ/sUexY4cURkyAhgeBK+etgF84sKFpi6yBzQUoCwexWN63esgu3rfCNQzNBUgLlNIssologr/lNBzEWSQkE/zhaKZt2byjKbzaI0UhLIJhvV/4ywAnbbvxmgxiNxATKaeZS1MG+aLCulrKIqZGYQHDXNT7yG+w90XyQmkCwsja2TjQnJCcQ9BzPZx4IJdIT6LH7ET4TQeiQnkCww/MVj4kgdEhQIPjyM97SQGiRokBvvNcXXggxDVIUCJKdU3lLBKFDkgLBrGb4koaZIE2BclrP5CsRhA5pCgT3XPButHkgUYFgZ7UX/CSC0CFVgeCrnrzkgVAiWYEy6sbzkghCh2QFgmtOhqaOCIh0BYJltd/ykAhCh4QFgr5f0teBUCJlgV77rKavBKFDygLBJae/eagFoUHSAsFS3zd8VIMYj7QFgn44CpTISFwgPA0SG4kLBJedL/JTEWIcUhcIEqrjQzExEVCg7EdZunfyJhAM+wT7vhMRoQQ6H+FqTaxdB+iqnj+B3jWdxVdVCHcEEuiYjfeE2ITYKH+bk9oL8CcQPKiwn7e6EK4IJFCzUFWbU8XAEO0FeBQIfnMvOD49YjIEEqhMonrhRFntBfgUCGbXw8eqYiGQQPXGqhfmBGgvwKtA0Ducz9oQDggk0HLSd0tyyrntEdartBfgV6C3AYuU861f9Y9J47NipDCEugpb40dY/Nbq2M+vQJDqdoTRqH2DRSvCK+GdRVMi3H2gO0m7kwqe3B5xKKfGiudr7/3MifSUnmzXm2vq8lszohdB70S/ufou/wbFs1xs+f0EAphb/03tZOWSBw7LYkIEEkixIBQyhliTkjN03Cbm+SuMIaK783/KhabYC7kJEUigaKsx8K1t9N7JxXWIwr9Amc0qHGbnWc53+a4a0Y1AAlUZB1Atmln4zl97Af4FggcOXs8AciZ24L1mRDcCCVQuEcB+F7Owp7T2AgIIBGdLlY8Y7d/iP/5rRnQikEDtwph/U5mFb01yI1HNRve587H/O9MikEBnbNtv3OQ478i04nHaCwgiEEwNwDbSJkaoy/gzXYuwNxIrL9axXxiBFOE9cBQW08JRoPLv6VTIAS8u7TuSorMfKGEEgsyQKEHqRXTBUaDY2NiY0h6RC8Z7VTpOFVcggeCRJ7ayNyncv8IGt2JvL2e3p3uvWCiB4C8XPI02JdwFqrBZOdvmThVXMIHgKL6nYUq4C+S+UDn7oTJVXOEEgoSqDwSrGykId4G+KMveINxlb6ZfYQzTArBNkMngLtCr5sTB14G0oBt0UkiB4ItPsBdgU2HMfaCD0aPm0J6pCirQu3bYc5CpMOpGYsZ16riCCgRvmkwRsnrkPUYI9FMlKwK95tLFFVYgeFxT1y1whF+4C7TcatAGAguK/EQVV2CB4Ib7FmEDICq4C1RrODxhVib7UsUVWiA460x3qxwxDO4CldqtFGhfKaq4ggsE+12wG2ATwF2gepOVAs32o4orvECw1e0fwWMg3AVaVmzaKXI3rvQ8qrgmEAhWVvtX+CByx4irsBgHQkiJ8XS98phCIJjh+9QEUeSNMfeB0k5vPPSIMq5QAuW/Bf11ED7UEBjuAg3V0eMPNwQR6Hp3e5sGOzU2KL5slS5AHOQ9RjTnIFW/vUodVwiBrroseJa5t7rmDaqcvu0y+A+EvIe7QIqTY6qSwAWUTSaEEKj3fHaa4qipTHaPUD09NSLUGNeoPnlSzSLtqOIKIVAl1VjO9f/Q3Jjx0QDshFNAjBPo5ab/lShCFVcIgdxV7zQ3TMq3Na3ZYDRIOIwQ6M6SdsWLtf2Z7jpMCIG6KOu8b1/gyiut+XA0SDCMuBNNSny84hltXCEESnbZkAMXA2cU3P6y4Sj+gyEqOAuUWX/ZSx7iCnIZfybIzrnCzx9+3Lxo8LUA0RAWzgJdIWv4iCvQjcQ07T0rPK07UZBwiBFfYa078hHXJI8y3vM8cIxJ48kH7gKt9w78Zn4MA1VcEwsEz+sHeBT3/RnfnOcb7gJVzIUqrqkFeuPjPCAzKXiQaaPKAMkP92QgC3o9CxyleFPxsmnDWj6W+VbGh3TfBC+CBuYMomvJjXyAhb6V8QFdtjKXaK37DPnBtGEtH0t9K6Mg04bAvT8etLXDLoB5xmLfyijAExcPt8AyVZ07Y/sgfrHctzLyc9+pnq1H6fpBfVq9Mm1gS8eC38rIxzdfw9tb2Qr/wyMDH5s2soVjyW9laPLJduVsxEL4zueeaUNbNhb9VoYGPdcrZwPiAL6vRn8XAsnF0t7K0EVsR1b4Z64pzDTO/Zxpg1syRt6JTtlFeSZhaoEym4Qm/bvDV/VQfpvTPtNGt2C4C3Sn7XDYU5SU+0Nf6UIxtUCQMSvQrdU29coR540mDm+xcBeoq9sv0LTpjU50g+KYXKD8nK+A/QfxA3eBHOLghXUCbHCiiiuyQHDLewQ27eAD7gLZJ8Amq//gFx3jOL0n+5GeN7LEFgieNeuBN6V5gLtAbVucDAyGV53r6y1+PsLVmli7DtBVvegCQcanLZ+LnYMFwF2gC66k1FHwK7ZVX+ljNt4TYhNio/xtdLxJL75AkDPCN1XsHKSPEZfxb88+Ath8RW/pZqGqbjIUA0O0FzADgQDiKp4ROwXJY8x9oCfHtvxRSLcpZRLVCyfKai9gFgLBXucEsVOQOtwFyh5VghBSbpbe0vXGqhfmmHLIS+6cq0TZME72cBdostW4y88vRVrpbdu3nPTdkpxybnuE9SrtBcxEILhb94tMsXOQNNwF8opUziL1Nyhb48eOeEn81urYby4CQVpoMA7zTIERNxJ3KGc7yxVywJ2k3Ul3CmzLupGLrbkIBIrvPPFVDeMx4lHGSOVsSGFjprJsK/DQ/lg1TzXWsw2MawLWOu8QOwXpwlGg8+fPHyj/v21JW3u56b+OVx90UNces/kKYzlXZTw+1zASjgKR9zTVVzpMBWkdFqa9gFkJBI9CPuGjyxE5wlGg6+/R2zA0mFRpzEB8GjfWXsC8BIKMAf43xc5BmnA+B1JsGNTM/39L3ukvnRVZjn3UIZGvMJZFLnvETkGScBXonyakctuu3la1CmsVur3c6HdSEgiOV8ATISPgKNDbWp6/svPTdTwLGzP1VmCj21ISCB617ISP5znDUaDJpdUj4NwrN6mwIzK/cpCUQPBuWPXzYucgOTgKFDIkd31088KP2TJK54BL5igQk7ALXbdZMoSjQGXz/u4r7animqdAcNUv/I3YOUgLjgJVm5m7PqcaVVwzFQjS/leHfiAQOcFRoF55X1ztQ6nimqtAAD87rRY7BSnBUaBD1upuvJeS3VRxzVcg+NsvHEcZMxiu94EmkeYbL1zdFkoG0sU1Y4EgrX+ti2LnIBk434lOrMI+CHNYSjn8hDkLBLDG6QccXsMwuDfnyEnZtvFSIU8yCse8BYJbwR/hgL0GIZdufrnybkIF7IDBEFAgXRyqPOKt2DlIABRIJy/CfZLFzsH8QYH0sM45OrvwUvIGBdLH7VZB18TOwcxBgfSiiCsfja2E9IECFcI/TVrfEjsHcwYFKozs6PJL8K6iTlCgwrkeEqyzXZPsQYEMIHte+QV4OaYdFMggUlo0wuerWkGBDEOxymV8hthJmCMokKH8G1rzsNg5mCEokOHsrNKTdoQHywMF4sDrUS4r8Io+PygQJ5IbNrskdg7mBQrEDcUqtxHYkYcGKBBXHg+ouF7H99jDnb/Iro8PFIg7JwOaa30Ferpz245uETIbQAEFMoKcONehTz/Y+lON6k6VXQKHaDnAgkGBjOLZMOeFBV8sqOJyEOBSnZLyeqkMBTKSq51qbMq/xWozO71RxNDfqGWAAhnNrzXb57ukt2LHYwVFkd/FSUckUCDjefeDy8AH71eLjWKn64vfECsfUUCBaHg+1nFqXk9tbVx779g/0tFNXveqUSA6bvV1+1F9Nn3RqXPblt3cEvUfYGmgQLSc+6j6JtWHTkov57JtdAywZ7GgQPQcCGiwX+wcRAMF4oMDdYKPiJ2DSKBAvJC9osrHZ8VOQhRQIJ54F1ehjRxfpUeBeCM9xq2HvO5Cs6BAPPJmnlvon2InYWJQIF55u8C9i7zOhVAgnnm7qHJ7OT0NQ4F4J3NZ9ea7ZfM8Q0CBsh9l6d5pwQIxP/l6f/91en54S0Iogc5HuFoTa9cBuqq3aIEAFLtDqi6SRcsygQQ6ZuM9ITYhNsrfRsezIQsXiOFUqFPUQ7GTEB6BBGoWqurNQjEwRHsByxcIIGVIuYF/iZ2E0AgkUJncRg0nymovIAeBAB5Pc2u317LPpwUSqN5Y9cKcAO0F5CEQQMbKuj4/WfLJkEACLSd9tySnnNseYb1KewG5CMRwONRxjOU2cxXqKmyNHzsmC/Fbq2O/jAQCSB1X/pM9FtrZq3D3ge4k7U66U2Dbg7hcSvzItT5J83ZZgNfcD99FtAAEEyj9tvK/3Nt8V7J/D8qlmOxGtz3dzz78uNhJ8I9AAr0bUZRUP8UsxOs4TlZfYWqezqvpu8jShqYXSKBZJaataW6XigLlR3Goj334UYu6rhdIIO9pzLdXYDcU6AOeLKhdM9qCBrMTSCAbdrS2ZOszKJAWTkSU+3hLpthZ8IRAAtX4np3290tHgbSRtjLEeYRlNDwTSKDpJb9hrjieunUYjQJp5+ZUr9qz7oqdBT0CCZQ5wcabmf1Tm6BAulAcG+TYarnUO1wU7D5QjvJ/l+JYnPbdKBBLRmKofY8tku4UD5u0isyz+NYO/fdKt/kiCiQ+/8Y0cfryN4kOB4QCmQWpcwKdBx+UokMokLlwY1YDp4F7JXd7CAUyI27NDXYI2/JG7DQ4gQKZF//++FGZzsskNCgQCmR2PF/fyz44+m+x0zAQFMgcydg7tHK10QelcEKEApkrf05vbN99mdk/t0eBzJhHqz51qDfxd7O+y4gCmTfZx78JKBcalyp2HjpBgcyf/9aGu9QYuvWF2HloBQWSBIo/57S1axJ12PxGHkeBJEP6b5Ma2bX9/pR5nRKhQJLixfZR/mU7zT5tPhKhQJLjyZYRdcq0n3ncPO4SoUCS4vqghm1mpQM83Ta6gV3It3vEb8+IAkmJXc49Pu3TqvYz5crrfVEt7Py/WndL1JRQIAmR5VIhdOGMmtVH5215lzS/u7tb6Nzjb8XKCQWSEGdtljPTzCYu+TenrhveoFTD4WuuiZETCiQhVpVUzhKLf7jr7fF5vao4dJi848GH+wQFBZIQvxRTflP9XELH/oc7vm3vWKnb9L2PTZcTCiQhrpUKfw1wyc1bX6Ebm8a1tq/cddqvpnmQjwJJiUaNnDsGu1aPLaycImXj+I8cXdtP2PSP0B2joUBS4lbdxn17VxhuoBR3dkzt5lm60Zc/HhPwOSwKJCmyt343j9uYZC+PLR7UuLTHJxPXX3wnREYokBzIuZ44raePTe1e07Zc5fkxGgokHzLPr5/YxcvGr9eUjRd4axeCAsmN9D/XR/WobePVKfLno//RV4cCyZOsa9tnfRHkaN8w7P82JL+iqAgFkjVPTi6f2MO/lGuzz2dsTDbqWg0FQgDuHl46vntdu/KN+kQtP3KH060jFAjJ478Ta6b2a+peoka7wbM2njbsBAkFQgqSfmXXksgegeVL1eo45Pt1x+/p/URCgRBdvL68c/G43kHuxas0DZu4ZMf5J9oKoUBIYWTePLp6xpCP6zjY1AhsmlBgrAYUCDGY1BD3oNYuK/NtQ4EQgwmZmAVwpcpvmttQIMRQLnnmQAbAylDNjSgQYiiJ3VZ529j32e+ruREFQgzlUKXApOznMx0baW5EgRBDuW29n5kqajTV3IgCIYbya4DzlIMbQgLra25EgRBD2dEpdVTrXqv+qKe5EQVCDOW+o/J5/dQhmhtRIMRgRje/ApmLXW9pbkOBEIPJ/sGtvG3Hq/m2CShQ9iM97bdRIGnyuOCrHUIJdD7C1ZpYuw7QVT0KZCEIJNAxG+8JsQmxUf42J7UXQIEsBIEEahaqGvtKMTBEewEUyEIQSKAyieqFE2U1N1/omUuxBZzqQ8wVgQSqN1a9MCdAc/PTTblUPsSpPsRcEUig5aTvluSUc9sjrFdpL9D4FKf6EHNFqKuwNX6ExW+tjv0okIUg3H2gO0m7k+7o3IsCWQhi3YlGgSwEFAihAgVCqECBECpQIIQKFAihQjSBJsTlp0vfMB7o0YGPWsLa9OGjll7t+KglrE1vPmr5tC0ftYS1nV3g71ZBJIGWDipAyWo+PFChDB+1+JTw5KOWSqX5qMXHxoOPWirb8lGLj23bAn+3EYaOOMWzQB9QnZehQjb24qMWwz+X9bLjEz5q4enb/mBrPmqBlkY/w0SBOIMCaYICcQYF0gQF4gwKpAkKxBkUSBMUiDMokCYoEGdQIE1QIM6gQJoILZDPTT5qSezLRy1Q/y8+atnTjY9aoOkffNRytB0ftcBHx4w9UmiBtPYhy5ksfoZW4yeZ7OeFlzGApwo+alE85aMWimSEFgixcFAghAoUCKECBUKoQIEQKlAghAoUCKECBUKoQIEQKgIAkMkAAAQHSURBVFAghAoUCKECBUKoQIEQKoQVaEVA2ZDj1LVsUXZqFUFXyahIPjJS1UKXUOYM71K1Zr+jTCavFrpkXg7zsK2XAMYnI6hAa8nXid1sztNWM8slluEITRWKPbaR9Bnl1kKX0ISSM/dOKTGCMpm8WuiS6eny894Issv4ZAQVqE4fgGzvgbTVDGxLW8NWO0IiqTPKq4UqoRybScx0WrF0qmTe10KVzFPCjrXrG2b8b0ZIge6QLcx0ghttPS2HPTtH14jr+eXLlSKpM8qthS6hu97sF8Uacpsqmbxa6JK51T+VmbboZ/xvRkiBkshZZhprnU1ZTyXv4oSEPqarxCuSj4yUtfCQ0NvmXjn0vx62Fupk/js+ze6w8b8ZIQXaTVKYaQJ5RFdNhk27K6+2O3ahq0X5p6fOSFkLfULnAu1O0iejrIU6mWgbp35vjE9G2E+gc8w0zrrgSDBGEUPo2kWrP4EoM1J9AlEm9PRz626p1Mmoa6FNhkHRp4vxyQh7DrSdmUa58lLZHuV/EePxiuQjIw2BjE7omrvvGaBOJrcWumR2h7PTeJJjdDKCXoX5RjB6+w+grOVgyYPM9NuyOVS1qP70tBkpa6FLSFG7fbpqiSaZvFrokjmg/OAZ7G58MoIKtMZ63slBNrRv8+UEVJy955viP9LVohKINiNlLXQJHSej41neUiWTVwtdMu/8vVbvG1dksfG/GWHvRK+sV6a5jpHFOPDwc7fSDTdRVqL+8qHMSFULVUJxRMVDqmTe10L327nb1922/mp2ychk8FkYQgUKhFCBAiFUoEAIFSgQQgUKhFCBAiFUoEAIFSgQQgUKhFCBAiFUoEAIFSgQQgUKhFCBAiFUoEAIFSgQQgUKhFCBAiFUoEAIFSgQQgUKhFCBAiFUoEAIFSgQQgUKhFCBAhVGD/VLxGS62JmYJShQYZzavHlzpWBmciWN9Bc7GfMDBTIE397sNL3HYrETMT9QIENQCQQVYwA8Vn9V2XPJ/U7lq7C9K68IsPVdIWpqYoMCGYKmQB67c6ZZ+ZzJDrdJh4XFonYNs/pJ5OxEBQUyBE2BPge4T2YAHCLX0hymMVsHVRI3OXFBgQxBU6BZANlsp8qXydXT5OSTJ082WGWInJ6YoECGoCnQXFagbUqBNqov8Om6/5Q2KJAh6BDoELkncmLigwIZgg6BHhWPY7bG9BI3OXFBgQxBh0Awzm7u/ilF54icnaigQIagVaA690Ext3YpH8r+hyUOCoRQgQIhVKBACBUoEEIFCoRQgQIhVKBACBUoEEIFCoRQgQIhVKBACBUoEEIFCoRQgQIhVKBACBUoEEIFCoRQgQIhVKBACBUoEEIFCoRQgQIhVKBACBUoEEIFCoRQ8f+g8D/xTdoPSwAAAABJRU5ErkJggg==" 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" /><!-- --></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><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 did not converge:
 ## false convergence (8)</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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" 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre>
 <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre>
 <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre>
 <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
 ## doubtful</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.4 
+<pre><code>## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Thu Nov 19 14:46:15 2020 
-## Date of summary: Thu Nov 19 14:46:15 2020 
+## Date of fit:     Mon Feb 15 14:11:19 2021 
+## Date of summary: Mon Feb 15 14:11:19 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 380 model solutions performed in 0.085 s
+## Fitted using 369 model solutions performed in 0.082 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1724,22 +1698,22 @@ summary(m.L1.SFO)</code></pre>
 ## 
 ## Results:
 ## 
-##        AIC      BIC    logLik
-##   95.88778 99.44927 -43.94389
+##        AIC      BIC   logLik
+##   95.88781 99.44929 -43.9439
 ## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##           Estimate Std. Error  Lower  Upper
 ## parent_0     92.47     1.2820 89.720 95.220
-## log_alpha    16.92        NaN    NaN    NaN
-## log_beta     19.26        NaN    NaN    NaN
-## sigma         2.78     0.4501  1.814  3.745
+## log_alpha    13.78        NaN    NaN    NaN
+## log_beta     16.13        NaN    NaN    NaN
+## sigma         2.78     0.4598  1.794  3.766
 ## 
 ## Parameter correlation:
-##           parent_0 log_alpha log_beta    sigma
-## parent_0  1.000000       NaN      NaN 0.002218
-## log_alpha      NaN         1      NaN      NaN
-## log_beta       NaN       NaN        1      NaN
-## sigma     0.002218       NaN      NaN 1.000000
+##            parent_0 log_alpha log_beta     sigma
+## parent_0  1.0000000       NaN      NaN 0.0001671
+## log_alpha       NaN         1      NaN       NaN
+## log_beta        NaN       NaN        1       NaN
+## sigma     0.0001671       NaN      NaN 1.0000000
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1747,9 +1721,9 @@ summary(m.L1.SFO)</code></pre>
 ## for estimators of untransformed parameters.
 ##           Estimate t value Pr(&gt;t)  Lower  Upper
 ## parent_0 9.247e+01      NA     NA 89.720 95.220
-## alpha    2.223e+07      NA     NA     NA     NA
-## beta     2.325e+08      NA     NA     NA     NA
-## sigma    2.780e+00      NA     NA  1.814  3.745
+## alpha    9.658e+05      NA     NA     NA     NA
+## beta     1.010e+07      NA     NA     NA     NA
+## sigma    2.780e+00      NA     NA  1.794  3.766
 ## 
 ## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
@@ -1778,7 +1752,7 @@ FOCUS_2006_L2_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L2)</code></pre>
 <pre class="r"><code>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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" 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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>
@@ -1789,12 +1763,12 @@ plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,
 <pre class="r"><code>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 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" 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.4 
+<pre><code>## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Thu Nov 19 14:46:15 2020 
-## Date of summary: Thu Nov 19 14:46:15 2020 
+## Date of fit:     Mon Feb 15 14:11:19 2021 
+## Date of summary: Mon Feb 15 14:11:19 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -1836,10 +1810,10 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09
-## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.617e-07
-## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.386e-07
-## sigma     -7.436e-09 -1.617e-07 -1.386e-07  1.000e+00
+## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09
+## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.602e-07
+## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.372e-07
+## sigma     -7.828e-09 -1.602e-07 -1.372e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1867,12 +1841,12 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 <pre class="r"><code>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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" 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.4 
+<pre><code>## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Thu Nov 19 14:46:15 2020 
-## Date of summary: Thu Nov 19 14:46:15 2020 
+## Date of fit:     Mon Feb 15 14:11:20 2021 
+## Date of summary: Mon Feb 15 14:11:20 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1912,18 +1886,18 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##          Estimate Std. Error      Lower     Upper
 ## parent_0   93.950  9.998e-01    91.5900   96.3100
-## log_k1      3.117  1.929e+03 -4558.0000 4564.0000
+## log_k1      3.112  1.842e+03 -4353.0000 4359.0000
 ## log_k2     -1.088  6.285e-02    -1.2370   -0.9394
 ## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638
 ## sigma       1.414  2.886e-01     0.7314    2.0960
 ## 
 ## Parameter correlation:
-##           parent_0     log_k1     log_k2   g_qlogis      sigma
-## parent_0 1.000e+00  6.459e-07  9.147e-11  2.665e-01  8.413e-11
-## log_k1   6.459e-07  1.000e+00  1.061e-04 -2.087e-04 -9.802e-06
-## log_k2   9.147e-11  1.061e-04  1.000e+00 -7.903e-01 -2.429e-09
-## g_qlogis 2.665e-01 -2.087e-04 -7.903e-01  1.000e+00  4.049e-09
-## sigma    8.413e-11 -9.802e-06 -2.429e-09  4.049e-09  1.000e+00
+##            parent_0     log_k1     log_k2   g_qlogis      sigma
+## parent_0  1.000e+00  6.783e-07 -3.390e-10  2.665e-01 -2.967e-10
+## log_k1    6.783e-07  1.000e+00  1.116e-04 -2.196e-04 -1.031e-05
+## log_k2   -3.390e-10  1.116e-04  1.000e+00 -7.903e-01  2.917e-09
+## g_qlogis  2.665e-01 -2.196e-04 -7.903e-01  1.000e+00 -4.408e-09
+## sigma    -2.967e-10 -1.031e-05  2.917e-09 -4.408e-09  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1931,7 +1905,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## for estimators of untransformed parameters.
 ##          Estimate   t value    Pr(&gt;t)   Lower   Upper
 ## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100
-## k1        22.5800 5.303e-04 4.998e-01  0.0000     Inf
+## k1        22.4800 5.553e-04 4.998e-01  0.0000     Inf
 ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909
 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591
 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960
@@ -1943,7 +1917,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50back DT50_k1 DT50_k2
-## parent 0.5335 5.311    1.599  0.0307   2.058</code></pre>
+## parent 0.5335 5.311    1.599 0.03084   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>
@@ -1961,7 +1935,7 @@ FOCUS_2006_L3_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L3)</code></pre>
 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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" 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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">
@@ -1969,10 +1943,10 @@ 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 used for fitting:    0.9.50.4 
+<pre><code>## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Thu Nov 19 14:46:16 2020 
-## Date of summary: Thu Nov 19 14:46:16 2020 
+## Date of fit:     Mon Feb 15 14:11:20 2021 
+## Date of summary: Mon Feb 15 14:11:20 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -2019,11 +1993,11 @@ plot(mm.L3)</code></pre>
 ## 
 ## Parameter correlation:
 ##            parent_0     log_k1     log_k2   g_qlogis      sigma
-## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.671e-08
-## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.148e-07
+## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.664e-08
+## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.147e-07
 ## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06
-## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.929e-07
-## sigma    -9.671e-08  7.148e-07  1.022e-06 -7.929e-07  1.000e+00
+## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.926e-07
+## sigma    -9.664e-08  7.147e-07  1.022e-06 -7.926e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -2056,7 +2030,7 @@ plot(mm.L3)</code></pre>
 ##    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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" 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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>
@@ -2074,13 +2048,13 @@ 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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" 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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 used for fitting:    0.9.50.4 
+<pre><code>## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Thu Nov 19 14:46:16 2020 
-## Date of summary: Thu Nov 19 14:46:16 2020 
+## Date of fit:     Mon Feb 15 14:11:20 2021 
+## Date of summary: Mon Feb 15 14:11:20 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -2141,10 +2115,10 @@ 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 used for fitting:    0.9.50.4 
+<pre><code>## mkin version used for fitting:    1.0.3 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Thu Nov 19 14:46:16 2020 
-## Date of summary: Thu Nov 19 14:46:16 2020 
+## Date of fit:     Mon Feb 15 14:11:20 2021 
+## Date of summary: Mon Feb 15 14:11:20 2021 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -2186,10 +2160,10 @@ plot(mm.L4)</code></pre>
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07
-## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.169e-08
-## log_beta  -5.543e-01  9.889e-01  1.000e+00  4.910e-08
-## sigma     -2.456e-07  2.169e-08  4.910e-08  1.000e+00
+## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07
+## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.478e-08
+## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.211e-08
+## sigma     -2.468e-07  2.478e-08  5.211e-08  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -2211,8 +2185,8 @@ plot(mm.L4)</code></pre>
 ## parent 108.9 1644    494.9</code></pre>
 </div>
 <div id="references" class="section level1 unnumbered">
-<h1>References</h1>
-<div id="refs" class="references">
+<h1 class="unnumbered">References</h1>
+<div id="refs" class="references hanging-indent">
 <div id="ref-ranke2014">
 <p>Ranke, Johannes. 2014. “Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0.” Umweltbundesamt Projektnummer 27452.</p>
 </div>