Static documentation rebuilt by staticdocs::build_site()

10 files changed, 68 insertions, 68 deletions

diff --git a/inst/web/Extract.mmkin.html b/inst/web/Extract.mmkin.html
index 60697959..81a7a663 100644
--- a/inst/web/Extract.mmkin.html
+++ b/inst/web/Extract.mmkin.html
@@ -181,7 +181,7 @@ $calls
 
 $time
    user  system elapsed 
-  0.704   0.000   0.702 
+  0.696   0.000   0.695 
 
 $mkinmod
 <mkinmod> model generated with
@@ -367,7 +367,7 @@ function (P)
     }
     return(mC)
 }
-<environment: 0x49ead78>
+<environment: 0x3431d78>
 
 $cost_notrans
 function (P) 
@@ -389,7 +389,7 @@ function (P)
         scaleVar = scaleVar)
     return(mC)
 }
-<environment: 0x49ead78>
+<environment: 0x3431d78>
 
 $hessian_notrans
            parent_0     alpha       beta
@@ -455,7 +455,7 @@ $bparms.state
 99.66619 
 
 $date
-[1] "Tue Jun 28 01:17:01 2016"
+[1] "Tue Jun 28 01:32:02 2016"
 
 attr(,"class")
 [1] "mkinfit" "modFit" 
@@ -540,7 +540,7 @@ $calls
 
 $time
    user  system elapsed 
-  0.208   0.004   0.208 
+  0.200   0.000   0.204 
 
 $mkinmod
 <mkinmod> model generated with
@@ -727,7 +727,7 @@ function (P)
     }
     return(mC)
 }
-<environment: 0x5a6c3b0>
+<environment: 0x44b33b0>
 
 $cost_notrans
 function (P) 
@@ -749,7 +749,7 @@ function (P)
         scaleVar = scaleVar)
     return(mC)
 }
-<environment: 0x5a6c3b0>
+<environment: 0x44b33b0>
 
 $hessian_notrans
                   parent_0 k_parent_sink
@@ -812,7 +812,7 @@ $bparms.state
 99.17407 
 
 $date
-[1] "Tue Jun 28 01:17:00 2016"
+[1] "Tue Jun 28 01:32:02 2016"
 
 attr(,"class")
 [1] "mkinfit" "modFit" 
@@ -890,7 +890,7 @@ $calls
 
 $time
    user  system elapsed 
-  0.208   0.004   0.208 
+  0.200   0.000   0.204 
 
 $mkinmod
 <mkinmod> model generated with
@@ -1077,7 +1077,7 @@ function (P)
     }
     return(mC)
 }
-<environment: 0x5a6c3b0>
+<environment: 0x44b33b0>
 
 $cost_notrans
 function (P) 
@@ -1099,7 +1099,7 @@ function (P)
         scaleVar = scaleVar)
     return(mC)
 }
-<environment: 0x5a6c3b0>
+<environment: 0x44b33b0>
 
 $hessian_notrans
                   parent_0 k_parent_sink
@@ -1162,7 +1162,7 @@ $bparms.state
 99.17407 
 
 $date
-[1] "Tue Jun 28 01:17:00 2016"
+[1] "Tue Jun 28 01:32:02 2016"
 
 attr(,"class")
 [1] "mkinfit" "modFit" 
diff --git a/inst/web/mccall81_245T.html b/inst/web/mccall81_245T.html
index 6596bd70..f073876e 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 01:17:19 2016 
-Date of summary: Tue Jun 28 01:17:19 2016 
+Date of fit:     Tue Jun 28 01:32:20 2016 
+Date of summary: Tue Jun 28 01:32:20 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 3.778 s
+Fitted with method Port using 246 model solutions performed in 3.912 s
 
 Weighting: none
 
diff --git a/inst/web/mkinfit.html b/inst/web/mkinfit.html
index dd7f1831..6ebf995c 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 01:17:26 2016 
-Date of summary: Tue Jun 28 01:17:26 2016 
+Date of fit:     Tue Jun 28 01:32:27 2016 
+Date of summary: Tue Jun 28 01:32:27 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 0.457 s
+Fitted with method Port using 64 model solutions performed in 0.425 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 
-  2.868   2.284   2.301 
+  2.880   2.292   2.309 
 </div>
 <div class='input'>coef(fit)
 </div>
diff --git a/inst/web/mkinpredict.html b/inst/web/mkinpredict.html
index d64a075f..06980d3f 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 
-  0.024   0.048   0.010 
+  0.020   0.048   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 
-  0.008   0.020   0.005 
+  0.004   0.000   0.004 
 </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),
@@ -326,7 +326,7 @@
 201   20 4.978707 27.46227
 </div>
 <div class='output'>   user  system elapsed 
-  0.132   0.004   0.138 
+  0.140   0.000   0.139 
 </div></pre>
   </div>
   <div class="span4">
diff --git a/inst/web/summary.mkinfit.html b/inst/web/summary.mkinfit.html
index 8ade3a21..b8a6d507 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 01:17:46 2016 
-Date of summary: Tue Jun 28 01:17:46 2016 
+Date of fit:     Tue Jun 28 01:32:47 2016 
+Date of summary: Tue Jun 28 01:32:47 2016 
 
 Equations:
 d_parent = - k_parent_sink * parent
 
 Model predictions using solution type analytical 
 
-Fitted with method Port using 35 model solutions performed in 0.27 s
+Fitted with method Port using 35 model solutions performed in 0.242 s
 
 Weighting: none
 
diff --git a/inst/web/transform_odeparms.html b/inst/web/transform_odeparms.html
index 73ff0d08..69e52241 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 01:17:49 2016 
-Date of summary: Tue Jun 28 01:17:49 2016 
+Date of fit:     Tue Jun 28 01:32:50 2016 
+Date of summary: Tue Jun 28 01:32:50 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 1.639 s
+Fitted with method Port using 153 model solutions performed in 1.694 s
 
 Weighting: none
 
diff --git a/inst/web/vignettes/FOCUS_D.html b/inst/web/vignettes/FOCUS_D.html
index 48ae4654..7f564ad8 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 01:22:20 2016 
-## Date of summary: Tue Jun 28 01:22:21 2016 
+## Date of fit:     Tue Jun 28 01:37:23 2016 
+## Date of summary: Tue Jun 28 01:37:23 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 1.759 s
+## Fitted with method Port using 153 model solutions performed in 1.704 s
 ## 
 ## Weighting: none
 ## 
diff --git a/inst/web/vignettes/FOCUS_L.html b/inst/web/vignettes/FOCUS_L.html
index 7026e3ce..154ac896 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 01:22:22 2016 
-## Date of summary: Tue Jun 28 01:22:22 2016 
+## Date of fit:     Tue Jun 28 01:37:25 2016 
+## Date of summary: Tue Jun 28 01:37:25 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 37 model solutions performed in 0.253 s
+## Fitted with method Port using 37 model solutions performed in 0.244 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 01:22:24 2016 
-## Date of summary: Tue Jun 28 01:22:24 2016 
+## Date of fit:     Tue Jun 28 01:37:27 2016 
+## Date of summary: Tue Jun 28 01:37:27 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 1.241 s
+## Fitted with method Port using 188 model solutions performed in 1.236 s
 ## 
 ## Weighting: none
 ## 
@@ -425,15 +425,15 @@ 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 01:22:27 2016 
-## Date of summary: Tue Jun 28 01:22:27 2016 
+## Date of fit:     Tue Jun 28 01:37:29 2016 
+## Date of summary: Tue Jun 28 01:37:29 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 0.541 s
+## Fitted with method Port using 81 model solutions performed in 0.534 s
 ## 
 ## Weighting: none
 ## 
@@ -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 01:22:30 2016 
-## Date of summary: Tue Jun 28 01:22:30 2016 
+## Date of fit:     Tue Jun 28 01:37:32 2016 
+## Date of summary: Tue Jun 28 01:37:32 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 2.29 s
+## Fitted with method Port using 336 model solutions performed in 2.323 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 01:22:33 2016 
-## Date of summary: Tue Jun 28 01:22:33 2016 
+## Date of fit:     Tue Jun 28 01:37:35 2016 
+## Date of summary: Tue Jun 28 01:37:35 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 0.959 s
+## Fitted with method Port using 137 model solutions performed in 0.909 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 01:22:34 2016 
-## Date of summary: Tue Jun 28 01:22:35 2016 
+## Date of fit:     Tue Jun 28 01:37:36 2016 
+## Date of summary: Tue Jun 28 01:37:37 2016 
 ## 
 ## Equations:
 ## d_parent = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method Port using 46 model solutions performed in 0.302 s
+## Fitted with method Port using 46 model solutions performed in 0.312 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 01:22:35 2016 
-## Date of summary: Tue Jun 28 01:22:35 2016 
+## Date of fit:     Tue Jun 28 01:37:37 2016 
+## Date of summary: Tue Jun 28 01:37:37 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 0.438 s
+## Fitted with method Port using 66 model solutions performed in 0.417 s
 ## 
 ## Weighting: none
 ## 
diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf
index d21f7cb9..ac3af921 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 3a81c6c3..9e2dff36 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 25.634645 25.717260 25.925956 25.799875 26.071612
-##       Eigenvalue based  2.235689  2.237675  2.246796  2.239661  2.252349
-##      deSolve, compiled  1.869556  1.881345  1.887495  1.893133  1.896465
+##  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
 ##        max neval cld
-##  26.343349     3   b
-##   2.265038     3  a 
-##   1.899797     3  a</code></pre>
+##  25.736998     3   b
+##   2.262771     3  a 
+##   1.880606     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 13.6 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 13.8 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>
 <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 13.628133
-## Eigenvalue based       1.183044
+## deSolve, not compiled 13.750973
+## Eigenvalue based       1.207962
 ## 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 53.993961 54.012379 54.225054 54.030797 54.340600
-##      deSolve, compiled  3.450737  3.535551  3.583034  3.620364  3.649183
+##                   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
 ##        max neval cld
-##  54.650403     3   b
-##   3.678001     3  a</code></pre>
+##  55.105678     3   b
+##   3.722071     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)