1 files changed, 10 insertions, 10 deletions
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index 1f3c6677..964c1ad8 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -10,7 +10,7 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2016-03-23" />
+<meta name="date" content="2016-03-24" />
 
 <title>Example evaluation of FOCUS Example Dataset D</title>
 
@@ -64,7 +64,7 @@ img {
 <div id="header">
 <h1 class="title">Example evaluation of FOCUS Example Dataset D</h1>
 <h4 class="author"><em>Johannes Ranke</em></h4>
-<h4 class="date"><em>2016-03-23</em></h4>
+<h4 class="date"><em>2016-03-24</em></h4>
 </div>
 
 
@@ -135,10 +135,10 @@ print(FOCUS_2006_D)</code></pre>
 <p><img 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" title alt width="672" /></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:    0.9.40 
-## R version:       3.2.1 
-## Date of fit:     Mon Jul 20 14:51:06 2015 
-## Date of summary: Mon Jul 20 14:51:06 2015 
+<pre><code>## mkin version:    0.9.42 
+## R version:       3.2.4 
+## Date of fit:     Thu Mar 24 08:30:54 2016 
+## Date of summary: Thu Mar 24 08:30:54 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
@@ -146,7 +146,7 @@ print(FOCUS_2006_D)</code></pre>
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted with method Port using 153 model solutions performed in 0.66 s
+## Fitted with method Port using 153 model solutions performed in 0.659 s
 ## 
 ## Weighting: none
 ## 
@@ -185,9 +185,9 @@ print(FOCUS_2006_D)</code></pre>
 ## Residual standard error: 3.211 on 36 degrees of freedom
 ## 
 ## Backtransformed parameters:
-##   Confidence intervals for internally transformed parameters are asymmetric.
-##   t-test (unrealistically) based on the assumption of normal distribution
-##   for estimators of untransformed parameters.
+## Confidence intervals for internally transformed parameters are asymmetric.
+## 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      99.600000  61.720 2.024e-38 96.330000 1.029e+02
 ## k_parent_sink  0.047920  12.780 3.050e-15  0.040890 5.616e-02