Static documentation rebuilt by staticdocs::build_site()

14 files changed, 105 insertions, 105 deletions

diff --git a/inst/web/Extract.mmkin.html b/inst/web/Extract.mmkin.html
index 81a7a663..d165e11a 100644
--- a/inst/web/Extract.mmkin.html
+++ b/inst/web/Extract.mmkin.html
@@ -181,7 +181,7 @@ $calls
 
 $time
    user  system elapsed 
-  0.696   0.000   0.695 
+  0.712   0.000   0.711 
 
 $mkinmod
 <mkinmod> model generated with
@@ -367,7 +367,7 @@ function (P)
     }
     return(mC)
 }
-<environment: 0x3431d78>
+<environment: 0x498a868>
 
 $cost_notrans
 function (P) 
@@ -389,7 +389,7 @@ function (P)
         scaleVar = scaleVar)
     return(mC)
 }
-<environment: 0x3431d78>
+<environment: 0x498a868>
 
 $hessian_notrans
            parent_0     alpha       beta
@@ -455,7 +455,7 @@ $bparms.state
 99.66619 
 
 $date
-[1] "Tue Jun 28 01:32:02 2016"
+[1] "Tue Jun 28 01:54:25 2016"
 
 attr(,"class")
 [1] "mkinfit" "modFit" 
@@ -540,7 +540,7 @@ $calls
 
 $time
    user  system elapsed 
-  0.200   0.000   0.204 
+  0.224   0.000   0.224 
 
 $mkinmod
 <mkinmod> model generated with
@@ -727,7 +727,7 @@ function (P)
     }
     return(mC)
 }
-<environment: 0x44b33b0>
+<environment: 0x5658dc0>
 
 $cost_notrans
 function (P) 
@@ -749,7 +749,7 @@ function (P)
         scaleVar = scaleVar)
     return(mC)
 }
-<environment: 0x44b33b0>
+<environment: 0x5658dc0>
 
 $hessian_notrans
                   parent_0 k_parent_sink
@@ -812,7 +812,7 @@ $bparms.state
 99.17407 
 
 $date
-[1] "Tue Jun 28 01:32:02 2016"
+[1] "Tue Jun 28 01:54:24 2016"
 
 attr(,"class")
 [1] "mkinfit" "modFit" 
@@ -890,7 +890,7 @@ $calls
 
 $time
    user  system elapsed 
-  0.200   0.000   0.204 
+  0.224   0.000   0.224 
 
 $mkinmod
 <mkinmod> model generated with
@@ -1077,7 +1077,7 @@ function (P)
     }
     return(mC)
 }
-<environment: 0x44b33b0>
+<environment: 0x5658dc0>
 
 $cost_notrans
 function (P) 
@@ -1099,7 +1099,7 @@ function (P)
         scaleVar = scaleVar)
     return(mC)
 }
-<environment: 0x44b33b0>
+<environment: 0x5658dc0>
 
 $hessian_notrans
                   parent_0 k_parent_sink
@@ -1162,7 +1162,7 @@ $bparms.state
 99.17407 
 
 $date
-[1] "Tue Jun 28 01:32:02 2016"
+[1] "Tue Jun 28 01:54:24 2016"
 
 attr(,"class")
 [1] "mkinfit" "modFit" 
diff --git a/inst/web/mccall81_245T.html b/inst/web/mccall81_245T.html
index f073876e..20328b34 100644
--- a/inst/web/mccall81_245T.html
+++ b/inst/web/mccall81_245T.html
@@ -114,8 +114,8 @@
 </div>
 <div class='output'>mkin version:    0.9.43 
 R version:       3.3.1 
-Date of fit:     Tue Jun 28 01:32:20 2016 
-Date of summary: Tue Jun 28 01:32:20 2016 
+Date of fit:     Tue Jun 28 01:54:43 2016 
+Date of summary: Tue Jun 28 01:54:43 2016 
 
 Equations:
 d_T245 = - k_T245_sink * T245 - k_T245_phenol * T245
@@ -124,7 +124,7 @@ d_anisole = + k_phenol_anisole * phenol - k_anisole_sink * anisole
 
 Model predictions using solution type deSolve 
 
-Fitted with method Port using 246 model solutions performed in 3.912 s
+Fitted with method Port using 246 model solutions performed in 3.74 s
 
 Weighting: none
 
diff --git a/inst/web/mkinfit.html b/inst/web/mkinfit.html
index 6ebf995c..9e0162c5 100644
--- a/inst/web/mkinfit.html
+++ b/inst/web/mkinfit.html
@@ -325,15 +325,15 @@ summary(fit)
 </div>
 <div class='output'>mkin version:    0.9.43 
 R version:       3.3.1 
-Date of fit:     Tue Jun 28 01:32:27 2016 
-Date of summary: Tue Jun 28 01:32:27 2016 
+Date of fit:     Tue Jun 28 01:54:49 2016 
+Date of summary: Tue Jun 28 01:54:49 2016 
 
 Equations:
 d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
 
 Model predictions using solution type analytical 
 
-Fitted with method Port using 64 model solutions performed in 0.425 s
+Fitted with method Port using 64 model solutions performed in 0.433 s
 
 Weighting: none
 
@@ -409,7 +409,7 @@ print(system.time(fit &lt;- mkinfit(SFO_SFO, FOCUS_2006_D,
                            solution_type = &quot;eigen&quot;, quiet = TRUE)))
 </div>
 <div class='output'>   user  system elapsed 
-  2.880   2.292   2.309 
+  2.788   2.280   2.257 
 </div>
 <div class='input'>coef(fit)
 </div>
diff --git a/inst/web/mkinpredict.html b/inst/web/mkinpredict.html
index 06980d3f..be761407 100644
--- a/inst/web/mkinpredict.html
+++ b/inst/web/mkinpredict.html
@@ -304,7 +304,7 @@
 201   20 4.978707 27.46227
 </div>
 <div class='output'>   user  system elapsed 
-  0.020   0.048   0.011 
+  0.028   0.044   0.011 
 </div>
 <div class='input'>  system.time(
     print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
@@ -315,7 +315,7 @@
 201   20 4.978707 27.46227
 </div>
 <div class='output'>   user  system elapsed 
-  0.004   0.000   0.004 
+  0.024   0.000   0.005 
 </div>
 <div class='input'>  system.time(
     print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
diff --git a/inst/web/summary.mkinfit.html b/inst/web/summary.mkinfit.html
index b8a6d507..724e470d 100644
--- a/inst/web/summary.mkinfit.html
+++ b/inst/web/summary.mkinfit.html
@@ -159,15 +159,15 @@
 </div>
 <div class='output'>mkin version:    0.9.43 
 R version:       3.3.1 
-Date of fit:     Tue Jun 28 01:32:47 2016 
-Date of summary: Tue Jun 28 01:32:47 2016 
+Date of fit:     Tue Jun 28 01:55:10 2016 
+Date of summary: Tue Jun 28 01:55:10 2016 
 
 Equations:
 d_parent = - k_parent_sink * parent
 
 Model predictions using solution type analytical 
 
-Fitted with method Port using 35 model solutions performed in 0.242 s
+Fitted with method Port using 35 model solutions performed in 0.244 s
 
 Weighting: none
 
diff --git a/inst/web/transform_odeparms.html b/inst/web/transform_odeparms.html
index 69e52241..aa7e22f3 100644
--- a/inst/web/transform_odeparms.html
+++ b/inst/web/transform_odeparms.html
@@ -135,8 +135,8 @@ summary(fit, data=FALSE) # See transformed and backtransformed parameters
 </div>
 <div class='output'>mkin version:    0.9.43 
 R version:       3.3.1 
-Date of fit:     Tue Jun 28 01:32:50 2016 
-Date of summary: Tue Jun 28 01:32:50 2016 
+Date of fit:     Tue Jun 28 01:55:12 2016 
+Date of summary: Tue Jun 28 01:55:12 2016 
 
 Equations:
 d_parent = - k_parent_sink * parent - k_parent_m1 * parent
@@ -144,7 +144,7 @@ d_m1 = + k_parent_m1 * parent - k_m1_sink * m1
 
 Model predictions using solution type deSolve 
 
-Fitted with method Port using 153 model solutions performed in 1.694 s
+Fitted with method Port using 153 model solutions performed in 1.624 s
 
 Weighting: none
 
diff --git a/inst/web/vignettes/FOCUS_D.html b/inst/web/vignettes/FOCUS_D.html
index 7f564ad8..e01c5869 100644
--- a/inst/web/vignettes/FOCUS_D.html
+++ b/inst/web/vignettes/FOCUS_D.html
@@ -192,8 +192,8 @@ print(FOCUS_2006_D)</code></pre>
 <pre class="r"><code>summary(fit)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:23 2016 
-## Date of summary: Tue Jun 28 01:37:23 2016 
+## Date of fit:     Tue Jun 28 01:59:43 2016 
+## Date of summary: Tue Jun 28 01:59:44 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
@@ -201,7 +201,7 @@ print(FOCUS_2006_D)</code></pre>
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted with method Port using 153 model solutions performed in 1.704 s
+## Fitted with method Port using 153 model solutions performed in 1.698 s
 ## 
 ## Weighting: none
 ## 
diff --git a/inst/web/vignettes/FOCUS_L.html b/inst/web/vignettes/FOCUS_L.html
index 154ac896..828d9def 100644
--- a/inst/web/vignettes/FOCUS_L.html
+++ b/inst/web/vignettes/FOCUS_L.html
@@ -235,15 +235,15 @@ FOCUS_2006_L1_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L1)</code></pre>
 summary(m.L1.SFO)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:25 2016 
-## Date of summary: Tue Jun 28 01:37:25 2016 
+## Date of fit:     Tue Jun 28 01:59:45 2016 
+## Date of summary: Tue Jun 28 01:59:45 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.244 s
+## Fitted with method Port using 37 model solutions performed in 0.243 s
 ## 
 ## Weighting: none
 ## 
@@ -328,8 +328,8 @@ summary(m.L1.SFO)</code></pre>
 <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:27 2016 
-## Date of summary: Tue Jun 28 01:37:27 2016 
+## Date of fit:     Tue Jun 28 01:59:47 2016 
+## Date of summary: Tue Jun 28 01:59:47 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 1.236 s
+## Fitted with method Port using 188 model solutions performed in 1.241 s
 ## 
 ## Weighting: none
 ## 
@@ -425,8 +425,8 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:29 2016 
-## Date of summary: Tue Jun 28 01:37:29 2016 
+## Date of fit:     Tue Jun 28 01:59:49 2016 
+## Date of summary: Tue Jun 28 01:59:49 2016 
 ## 
 ## Equations:
 ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -495,8 +495,8 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:32 2016 
-## Date of summary: Tue Jun 28 01:37:32 2016 
+## Date of fit:     Tue Jun 28 01:59:52 2016 
+## Date of summary: Tue Jun 28 01:59:52 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 2.323 s
+## Fitted with method Port using 336 model solutions performed in 2.286 s
 ## 
 ## Weighting: none
 ## 
@@ -584,8 +584,8 @@ plot(mm.L3)</code></pre>
 <pre class="r"><code>summary(mm.L3[[&quot;DFOP&quot;, 1]])</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:35 2016 
-## Date of summary: Tue Jun 28 01:37:35 2016 
+## Date of fit:     Tue Jun 28 01:59:55 2016 
+## Date of summary: Tue Jun 28 01:59:56 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 0.909 s
+## Fitted with method Port using 137 model solutions performed in 0.907 s
 ## 
 ## Weighting: none
 ## 
@@ -684,15 +684,15 @@ plot(mm.L4)</code></pre>
 <pre class="r"><code>summary(mm.L4[[&quot;SFO&quot;, 1]], data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:36 2016 
-## Date of summary: Tue Jun 28 01:37:37 2016 
+## Date of fit:     Tue Jun 28 01:59:57 2016 
+## Date of summary: Tue Jun 28 01:59:58 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.312 s
+## Fitted with method Port using 46 model solutions performed in 0.306 s
 ## 
 ## Weighting: none
 ## 
@@ -744,15 +744,15 @@ plot(mm.L4)</code></pre>
 <pre class="r"><code>summary(mm.L4[[&quot;FOMC&quot;, 1]], data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:37 2016 
-## Date of summary: Tue Jun 28 01:37:37 2016 
+## Date of fit:     Tue Jun 28 01:59:57 2016 
+## Date of summary: Tue Jun 28 01:59:58 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 0.417 s
+## Fitted with method Port using 66 model solutions performed in 0.42 s
 ## 
 ## Weighting: none
 ## 
diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf
index ac3af921..efda950f 100644
--- a/inst/web/vignettes/FOCUS_Z.pdf
+++ b/inst/web/vignettes/FOCUS_Z.pdf
diff --git a/inst/web/vignettes/compiled_models.html b/inst/web/vignettes/compiled_models.html
index 9e2dff36..5e426a7f 100644
--- a/inst/web/vignettes/compiled_models.html
+++ b/inst/web/vignettes/compiled_models.html
@@ -250,21 +250,21 @@ mb.1 &lt;- microbenchmark(
 print(mb.1)</code></pre>
 <pre><code>## Unit: seconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 25.160343 25.384579 25.502052 25.608814 25.672906
-##       Eigenvalue based  2.219737  2.234679  2.244043  2.249621  2.256196
-##      deSolve, compiled  1.825299  1.843813  1.856078  1.862327  1.871467
+##  deSolve, not compiled 25.042204 25.078629 25.467550 25.115054 25.680223
+##       Eigenvalue based  2.273059  2.277424  2.285719  2.281790  2.292049
+##      deSolve, compiled  1.878785  1.883750  1.891594  1.888716  1.897998
 ##        max neval cld
-##  25.736998     3   b
-##   2.262771     3  a 
-##   1.880606     3  a</code></pre>
+##  26.245391     3   b
+##   2.302308     3  a 
+##   1.907281     3  a</code></pre>
 <pre class="r"><code>autoplot(mb.1)</code></pre>
-<p><img 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" title alt width="672" /></p>
-<p>We see that using the compiled model is by a factor of 13.8 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>
+<p><img 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" title alt width="672" /></p>
+<p>We see that using the compiled model is by a factor of 13.3 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 13.750973
-## Eigenvalue based       1.207962
+## deSolve, not compiled 13.297425
+## Eigenvalue based       1.208117
 ## deSolve, compiled      1.000000</code></pre>
 </div>
 <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level2">
@@ -285,18 +285,18 @@ 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 54.725198 54.787875 54.893809 54.85055 54.978114
-##      deSolve, compiled  3.618315  3.644838  3.670582  3.67136  3.696716
+##                   expr      min        lq      mean    median        uq
+##  deSolve, not compiled 53.69252 53.938844 54.137601 54.185167 54.360141
+##      deSolve, compiled  3.42508  3.526298  3.588392  3.627516  3.670048
 ##        max neval cld
-##  55.105678     3   b
-##   3.722071     3  a</code></pre>
+##  54.535116     3   b
+##   3.712579     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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" 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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 on</p>
 <pre><code>## R version 3.3.1 (2016-06-21)
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index 7f564ad8..e01c5869 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -192,8 +192,8 @@ print(FOCUS_2006_D)</code></pre>
 <pre class="r"><code>summary(fit)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:23 2016 
-## Date of summary: Tue Jun 28 01:37:23 2016 
+## Date of fit:     Tue Jun 28 01:59:43 2016 
+## Date of summary: Tue Jun 28 01:59:44 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent
@@ -201,7 +201,7 @@ print(FOCUS_2006_D)</code></pre>
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted with method Port using 153 model solutions performed in 1.704 s
+## Fitted with method Port using 153 model solutions performed in 1.698 s
 ## 
 ## Weighting: none
 ## 
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 154ac896..828d9def 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -235,15 +235,15 @@ FOCUS_2006_L1_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L1)</code></pre>
 summary(m.L1.SFO)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:25 2016 
-## Date of summary: Tue Jun 28 01:37:25 2016 
+## Date of fit:     Tue Jun 28 01:59:45 2016 
+## Date of summary: Tue Jun 28 01:59:45 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.244 s
+## Fitted with method Port using 37 model solutions performed in 0.243 s
 ## 
 ## Weighting: none
 ## 
@@ -328,8 +328,8 @@ summary(m.L1.SFO)</code></pre>
 <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:27 2016 
-## Date of summary: Tue Jun 28 01:37:27 2016 
+## Date of fit:     Tue Jun 28 01:59:47 2016 
+## Date of summary: Tue Jun 28 01:59:47 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 1.236 s
+## Fitted with method Port using 188 model solutions performed in 1.241 s
 ## 
 ## Weighting: none
 ## 
@@ -425,8 +425,8 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:29 2016 
-## Date of summary: Tue Jun 28 01:37:29 2016 
+## Date of fit:     Tue Jun 28 01:59:49 2016 
+## Date of summary: Tue Jun 28 01:59:49 2016 
 ## 
 ## Equations:
 ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -495,8 +495,8 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:32 2016 
-## Date of summary: Tue Jun 28 01:37:32 2016 
+## Date of fit:     Tue Jun 28 01:59:52 2016 
+## Date of summary: Tue Jun 28 01:59:52 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 2.323 s
+## Fitted with method Port using 336 model solutions performed in 2.286 s
 ## 
 ## Weighting: none
 ## 
@@ -584,8 +584,8 @@ plot(mm.L3)</code></pre>
 <pre class="r"><code>summary(mm.L3[[&quot;DFOP&quot;, 1]])</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:35 2016 
-## Date of summary: Tue Jun 28 01:37:35 2016 
+## Date of fit:     Tue Jun 28 01:59:55 2016 
+## Date of summary: Tue Jun 28 01:59:56 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 0.909 s
+## Fitted with method Port using 137 model solutions performed in 0.907 s
 ## 
 ## Weighting: none
 ## 
@@ -684,15 +684,15 @@ plot(mm.L4)</code></pre>
 <pre class="r"><code>summary(mm.L4[[&quot;SFO&quot;, 1]], data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:36 2016 
-## Date of summary: Tue Jun 28 01:37:37 2016 
+## Date of fit:     Tue Jun 28 01:59:57 2016 
+## Date of summary: Tue Jun 28 01:59:58 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.312 s
+## Fitted with method Port using 46 model solutions performed in 0.306 s
 ## 
 ## Weighting: none
 ## 
@@ -744,15 +744,15 @@ plot(mm.L4)</code></pre>
 <pre class="r"><code>summary(mm.L4[[&quot;FOMC&quot;, 1]], data = FALSE)</code></pre>
 <pre><code>## mkin version:    0.9.43 
 ## R version:       3.3.1 
-## Date of fit:     Tue Jun 28 01:37:37 2016 
-## Date of summary: Tue Jun 28 01:37:37 2016 
+## Date of fit:     Tue Jun 28 01:59:57 2016 
+## Date of summary: Tue Jun 28 01:59:58 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 0.417 s
+## Fitted with method Port using 66 model solutions performed in 0.42 s
 ## 
 ## Weighting: none
 ## 
diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf
index ac3af921..efda950f 100644
--- a/vignettes/FOCUS_Z.pdf
+++ b/vignettes/FOCUS_Z.pdf
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index 9e2dff36..5e426a7f 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -250,21 +250,21 @@ mb.1 &lt;- microbenchmark(
 print(mb.1)</code></pre>
 <pre><code>## Unit: seconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 25.160343 25.384579 25.502052 25.608814 25.672906
-##       Eigenvalue based  2.219737  2.234679  2.244043  2.249621  2.256196
-##      deSolve, compiled  1.825299  1.843813  1.856078  1.862327  1.871467
+##  deSolve, not compiled 25.042204 25.078629 25.467550 25.115054 25.680223
+##       Eigenvalue based  2.273059  2.277424  2.285719  2.281790  2.292049
+##      deSolve, compiled  1.878785  1.883750  1.891594  1.888716  1.897998
 ##        max neval cld
-##  25.736998     3   b
-##   2.262771     3  a 
-##   1.880606     3  a</code></pre>
+##  26.245391     3   b
+##   2.302308     3  a 
+##   1.907281     3  a</code></pre>
 <pre class="r"><code>autoplot(mb.1)</code></pre>
-<p><img 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" title alt width="672" /></p>
-<p>We see that using the compiled model is by a factor of 13.8 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>
+<p><img 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" title alt width="672" /></p>
+<p>We see that using the compiled model is by a factor of 13.3 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 13.750973
-## Eigenvalue based       1.207962
+## deSolve, not compiled 13.297425
+## Eigenvalue based       1.208117
 ## deSolve, compiled      1.000000</code></pre>
 </div>
 <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level2">
@@ -285,18 +285,18 @@ 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 54.725198 54.787875 54.893809 54.85055 54.978114
-##      deSolve, compiled  3.618315  3.644838  3.670582  3.67136  3.696716
+##                   expr      min        lq      mean    median        uq
+##  deSolve, not compiled 53.69252 53.938844 54.137601 54.185167 54.360141
+##      deSolve, compiled  3.42508  3.526298  3.588392  3.627516  3.670048
 ##        max neval cld
-##  55.105678     3   b
-##   3.722071     3  a</code></pre>
+##  54.535116     3   b
+##   3.712579     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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" 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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 on</p>
 <pre><code>## R version 3.3.1 (2016-06-21)