Rebuild vignettes with the released package installed

6 files changed, 63 insertions, 59 deletions

diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index edcfc53d..074847f0 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -316,8 +316,8 @@ objects.</p>
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:54 2015 
-## Date of summary: Sun Jun 21 15:31:54 2015 
+## Date of fit:     Sun Jun 21 18:14:52 2015 
+## Date of summary: Sun Jun 21 18:14:52 2015 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
@@ -325,7 +325,7 @@ objects.</p>
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted with method Port using 153 model solutions performed in 0.611 s
+## Fitted with method Port using 153 model solutions performed in 0.733 s
 ## 
 ## Weighting: none
 ## 
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 84a91652..3782847e 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -238,15 +238,15 @@ summary(m.L1.SFO)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:55 2015 
-## Date of summary: Sun Jun 21 15:31:55 2015 
+## Date of fit:     Sun Jun 21 18:14:52 2015 
+## Date of summary: Sun Jun 21 18:14:52 2015 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.084 s
+## Fitted with method Port using 37 model solutions performed in 0.096 s
 ## 
 ## Weighting: none
 ## 
@@ -346,8 +346,8 @@ is checked.</p>
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:55 2015 
-## Date of summary: Sun Jun 21 15:31:55 2015 
+## Date of fit:     Sun Jun 21 18:14:53 2015 
+## Date of summary: Sun Jun 21 18:14:53 2015 
 ## 
 ## 
 ## Warning: Optimisation by method Port did not converge.
@@ -359,7 +359,7 @@ is checked.</p>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 188 model solutions performed in 0.406 s
+## Fitted with method Port using 188 model solutions performed in 0.486 s
 ## 
 ## Weighting: none
 ## 
@@ -448,15 +448,15 @@ summary(m.L2.SFO)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:55 2015 
-## Date of summary: Sun Jun 21 15:31:55 2015 
+## Date of fit:     Sun Jun 21 18:14:53 2015 
+## Date of summary: Sun Jun 21 18:14:53 2015 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 41 model solutions performed in 0.09 s
+## Fitted with method Port using 41 model solutions performed in 0.101 s
 ## 
 ## Weighting: none
 ## 
@@ -558,15 +558,15 @@ mkinresplot(m.L2.FOMC)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:56 2015 
-## Date of summary: Sun Jun 21 15:31:56 2015 
+## Date of fit:     Sun Jun 21 18:14:54 2015 
+## Date of summary: Sun Jun 21 18:14:54 2015 
 ## 
 ## 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 0.173 s
+## Fitted with method Port using 81 model solutions performed in 0.213 s
 ## 
 ## Weighting: none
 ## 
@@ -647,8 +647,8 @@ plot(m.L2.DFOP)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:58 2015 
-## Date of summary: Sun Jun 21 15:31:58 2015 
+## Date of fit:     Sun Jun 21 18:14:56 2015 
+## Date of summary: Sun Jun 21 18:14:56 2015 
 ## 
 ## Equations:
 ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -657,7 +657,7 @@ plot(m.L2.DFOP)
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 336 model solutions performed in 0.746 s
+## Fitted with method Port using 336 model solutions performed in 0.851 s
 ## 
 ## Weighting: none
 ## 
@@ -739,15 +739,15 @@ plot(m.L3.SFO)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:58 2015 
-## Date of summary: Sun Jun 21 15:31:58 2015 
+## Date of fit:     Sun Jun 21 18:14:56 2015 
+## Date of summary: Sun Jun 21 18:14:56 2015 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 43 model solutions performed in 0.089 s
+## Fitted with method Port using 43 model solutions performed in 0.103 s
 ## 
 ## Weighting: none
 ## 
@@ -825,15 +825,15 @@ plot(m.L3.FOMC)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:58 2015 
-## Date of summary: Sun Jun 21 15:31:58 2015 
+## Date of fit:     Sun Jun 21 18:14:56 2015 
+## Date of summary: Sun Jun 21 18:14:56 2015 
 ## 
 ## Equations:
 ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 83 model solutions performed in 0.181 s
+## Fitted with method Port using 83 model solutions performed in 0.197 s
 ## 
 ## Weighting: none
 ## 
@@ -901,8 +901,8 @@ plot(m.L3.DFOP)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:59 2015 
-## Date of summary: Sun Jun 21 15:31:59 2015 
+## Date of fit:     Sun Jun 21 18:14:57 2015 
+## Date of summary: Sun Jun 21 18:14:57 2015 
 ## 
 ## Equations:
 ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -911,7 +911,7 @@ plot(m.L3.DFOP)
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 137 model solutions performed in 0.309 s
+## Fitted with method Port using 137 model solutions performed in 0.341 s
 ## 
 ## Weighting: none
 ## 
@@ -1002,15 +1002,15 @@ plot(m.L4.SFO)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:59 2015 
-## Date of summary: Sun Jun 21 15:31:59 2015 
+## Date of fit:     Sun Jun 21 18:14:57 2015 
+## Date of summary: Sun Jun 21 18:14:57 2015 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.1 s
+## Fitted with method Port using 46 model solutions performed in 0.111 s
 ## 
 ## Weighting: none
 ## 
@@ -1077,15 +1077,15 @@ plot(m.L4.FOMC)
 
 <pre><code>## mkin version:    0.9.36 
 ## R version:       3.2.1 
-## Date of fit:     Sun Jun 21 15:31:59 2015 
-## Date of summary: Sun Jun 21 15:31:59 2015 
+## Date of fit:     Sun Jun 21 18:14:57 2015 
+## Date of summary: Sun Jun 21 18:14:57 2015 
 ## 
 ## 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 0.139 s
+## Fitted with method Port using 66 model solutions performed in 0.162 s
 ## 
 ## Weighting: none
 ## 
diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf
index 649410cf..a42e6e01 100644
--- a/vignettes/FOCUS_Z.pdf
+++ b/vignettes/FOCUS_Z.pdf
diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd
index 5d83cf3a..3b24acbb 100644
--- a/vignettes/compiled_models.Rmd
+++ b/vignettes/compiled_models.Rmd
@@ -53,9 +53,11 @@ rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, comp
 print(smb.1)

 ```

 

-We see that using the compiled model is almost a factor of 8 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:

+We see that using the compiled model is by a factor of 

+`r round(smb.1["deSolve, not compiled", "median"]/smb.1["deSolve, compiled", "median"], 1)` 

+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:

 

 ```{r}

 smb.1["median"]/smb.1["deSolve, compiled", "median"]

@@ -81,5 +83,7 @@ smb.2["median"]/smb.2["deSolve, compiled", "median"]
 

 ```

 

-Here we get a performance benefit of more than a factor of 10 using the version

-of the differential equation model compiled from C code using the inline package!

+Here we get a performance benefit of a factor of 

+`r round(smb.2["deSolve, not compiled", "median"]/smb.2["deSolve, compiled", "median"], 1)` 

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

+the inline package!

diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index e6f21b09..5fcd88fb 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -94,20 +94,20 @@ mb.1 &lt;- microbenchmark(
 smb.1 &lt;- summary(mb.1)[-1]
 rownames(smb.1) &lt;- c(&quot;deSolve, not compiled&quot;, &quot;Eigenvalue based&quot;, &quot;deSolve, compiled&quot;)
 print(smb.1)</code></pre>
-<pre><code>##                            min        lq      mean    median        uq
-## deSolve, not compiled 4969.585 5033.7311 5092.7389 5097.8773 5154.3160
-## Eigenvalue based       868.731  891.7239  909.6449  914.7169  930.1018
-## deSolve, compiled     4935.049 4935.4796 4968.2150 4935.9097 4984.7978
+<pre><code>##                             min        lq      mean    median        uq
+## deSolve, not compiled 6980.8002 6996.4739 7024.5611 7012.1476 7046.4415
+## Eigenvalue based       925.3350  928.9405  951.8405  932.5460  965.0932
+## deSolve, compiled      747.2635  761.9405  771.4339  776.6174  783.5191
 ##                             max neval
-## deSolve, not compiled 5210.7547     3
-## Eigenvalue based       945.4867     3
-## deSolve, compiled     5033.6858     3</code></pre>
-<p>We see that using the compiled model is almost a factor of 8 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>
+## deSolve, not compiled 7080.7354     3
+## Eigenvalue based       997.6404     3
+## deSolve, compiled      790.4207     3</code></pre>
+<p>We see that using the compiled model is by a factor of 9 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>smb.1[&quot;median&quot;]/smb.1[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
-<pre><code>##                          median
-## deSolve, not compiled 1.0328141
-## Eigenvalue based      0.1853188
-## deSolve, compiled     1.0000000</code></pre>
+<pre><code>##                         median
+## deSolve, not compiled 9.029089
+## Eigenvalue based      1.200779
+## deSolve, compiled     1.000000</code></pre>
 </div>
 <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level1">
 <h1>Benchmark for a model that can not be solved with Eigenvalues</h1>
@@ -124,16 +124,16 @@ smb.2 &lt;- summary(mb.2)[-1]
 rownames(smb.2) &lt;- c(&quot;deSolve, not compiled&quot;, &quot;deSolve, compiled&quot;)
 print(smb.2)</code></pre>
 <pre><code>##                             min        lq      mean    median        uq
-## deSolve, not compiled 11.745276 11.754288 11.820726 11.763300 11.858451
-## deSolve, compiled      1.385829  1.386407  1.400841  1.386985  1.408347
-##                            max neval
-## deSolve, not compiled 11.95360     3
-## deSolve, compiled      1.42971     3</code></pre>
+## deSolve, not compiled 14.127630 14.245064 14.298201 14.362497 14.383486
+## deSolve, compiled      1.354744  1.362167  1.366362  1.369589  1.372171
+##                             max neval
+## deSolve, not compiled 14.404474     3
+## deSolve, compiled      1.374752     3</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
-## deSolve, not compiled 8.4812
-## deSolve, compiled     1.0000</code></pre>
-<p>Here we get a performance benefit of more than a factor of 10 using the version of the differential equation model compiled from C code using the inline package!</p>
+<pre><code>##                         median
+## deSolve, not compiled 10.48672
+## deSolve, compiled      1.00000</code></pre>
+<p>Here we get a performance benefit of a factor of 10.5 using the version of the differential equation model compiled from C code using the inline package!</p>
 </div>
 
 
diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf
index 825bb36a..7454e688 100644
--- a/vignettes/mkin.pdf
+++ b/vignettes/mkin.pdf