diff options
Diffstat (limited to 'inst')
| -rw-r--r-- | inst/web/Extract.mmkin.html | 24 | ||||
| -rw-r--r-- | inst/web/mccall81_245T.html | 6 | ||||
| -rw-r--r-- | inst/web/mkinfit.html | 8 | ||||
| -rw-r--r-- | inst/web/mkinpredict.html | 6 | ||||
| -rw-r--r-- | inst/web/summary.mkinfit.html | 4 | ||||
| -rw-r--r-- | inst/web/transform_odeparms.html | 6 | ||||
| -rw-r--r-- | inst/web/vignettes/FOCUS_D.html | 6 | ||||
| -rw-r--r-- | inst/web/vignettes/FOCUS_L.html | 40 | ||||
| -rw-r--r-- | inst/web/vignettes/FOCUS_Z.pdf | bin | 238636 -> 238632 bytes | |||
| -rw-r--r-- | inst/web/vignettes/compiled_models.html | 32 |
10 files changed, 66 insertions, 66 deletions
diff --git a/inst/web/Extract.mmkin.html b/inst/web/Extract.mmkin.html index e4225ffd..6e32f5a9 100644 --- a/inst/web/Extract.mmkin.html +++ b/inst/web/Extract.mmkin.html @@ -183,7 +183,7 @@ $calls $time user system elapsed - 0.260 0.000 0.259 + 0.252 0.000 0.252 $mkinmod <mkinmod> model generated with @@ -369,7 +369,7 @@ function (P) } return(mC) } -<environment: 0x37c6c70> +<environment: 0x33d3c70> $cost_notrans function (P) @@ -391,7 +391,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x37c6c70> +<environment: 0x33d3c70> $hessian_notrans parent_0 alpha beta @@ -457,7 +457,7 @@ $bparms.state 99.66619 $date -[1] "Sat Sep 10 04:12:07 2016" +[1] "Sat Sep 10 05:19:30 2016" attr(,"class") [1] "mkinfit" "modFit" @@ -544,7 +544,7 @@ $calls $time user system elapsed - 0.068 0.000 0.069 + 0.068 0.000 0.067 $mkinmod <mkinmod> model generated with @@ -731,7 +731,7 @@ function (P) } return(mC) } -<environment: 0x4cb0ed0> +<environment: 0x48bdf40> $cost_notrans function (P) @@ -753,7 +753,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x4cb0ed0> +<environment: 0x48bdf40> $hessian_notrans parent_0 k_parent_sink @@ -816,7 +816,7 @@ $bparms.state 99.17407 $date -[1] "Sat Sep 10 04:12:07 2016" +[1] "Sat Sep 10 05:19:30 2016" attr(,"class") [1] "mkinfit" "modFit" @@ -896,7 +896,7 @@ $calls $time user system elapsed - 0.068 0.000 0.069 + 0.068 0.000 0.067 $mkinmod <mkinmod> model generated with @@ -1083,7 +1083,7 @@ function (P) } return(mC) } -<environment: 0x4cb0ed0> +<environment: 0x48bdf40> $cost_notrans function (P) @@ -1105,7 +1105,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x4cb0ed0> +<environment: 0x48bdf40> $hessian_notrans parent_0 k_parent_sink @@ -1168,7 +1168,7 @@ $bparms.state 99.17407 $date -[1] "Sat Sep 10 04:12:07 2016" +[1] "Sat Sep 10 05:19:30 2016" attr(,"class") [1] "mkinfit" "modFit" diff --git a/inst/web/mccall81_245T.html b/inst/web/mccall81_245T.html index 9972571a..66a8fce2 100644 --- a/inst/web/mccall81_245T.html +++ b/inst/web/mccall81_245T.html @@ -114,8 +114,8 @@ </div> <div class='output'>mkin version: 0.9.44.9000 R version: 3.3.1 -Date of fit: Sat Sep 10 04:12:13 2016 -Date of summary: Sat Sep 10 04:12:13 2016 +Date of fit: Sat Sep 10 05:19:35 2016 +Date of summary: Sat Sep 10 05:19:35 2016 Equations: d_T245 = - k_T245_sink * T245 - k_T245_phenol * T245 @@ -125,7 +125,7 @@ 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 1.456 s +Fitted with method Port using 246 model solutions performed in 1.434 s Weighting: none diff --git a/inst/web/mkinfit.html b/inst/web/mkinfit.html index 910a43ec..899f2cc7 100644 --- a/inst/web/mkinfit.html +++ b/inst/web/mkinfit.html @@ -325,15 +325,15 @@ summary(fit) </div> <div class='output'>mkin version: 0.9.44.9000 R version: 3.3.1 -Date of fit: Sat Sep 10 04:12:15 2016 -Date of summary: Sat Sep 10 04:12:15 2016 +Date of fit: Sat Sep 10 05:19:38 2016 +Date of summary: Sat Sep 10 05:19:38 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 0.156 s +Fitted with method Port using 64 model solutions performed in 0.148 s Weighting: none @@ -409,7 +409,7 @@ print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE))) </div> <div class='output'> user system elapsed - 1.228 1.216 0.923 + 1.236 1.188 0.911 </div> <div class='input'>coef(fit) </div> diff --git a/inst/web/mkinpredict.html b/inst/web/mkinpredict.html index fbf06007..d8edeb23 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 0.004 0.005 + 0.004 0.028 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), @@ -315,7 +315,7 @@ 201 20 4.978707 27.46227 </div> <div class='output'> user system elapsed - 0.000 0.020 0.002 + 0.016 0.004 0.003 </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 - 0.036 0.000 0.036 + 0.032 0.000 0.035 </div></pre> </div> <div class="span4"> diff --git a/inst/web/summary.mkinfit.html b/inst/web/summary.mkinfit.html index 936350df..53cf5f97 100644 --- a/inst/web/summary.mkinfit.html +++ b/inst/web/summary.mkinfit.html @@ -159,8 +159,8 @@ </div> <div class='output'>mkin version: 0.9.44.9000 R version: 3.3.1 -Date of fit: Sat Sep 10 04:12:23 2016 -Date of summary: Sat Sep 10 04:12:23 2016 +Date of fit: Sat Sep 10 05:19:45 2016 +Date of summary: Sat Sep 10 05:19:45 2016 Equations: d_parent = - k_parent_sink * parent diff --git a/inst/web/transform_odeparms.html b/inst/web/transform_odeparms.html index 43c6cdc2..d016bef5 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: 0.9.44.9000 R version: 3.3.1 -Date of fit: Sat Sep 10 04:12:24 2016 -Date of summary: Sat Sep 10 04:12:24 2016 +Date of fit: Sat Sep 10 05:19:46 2016 +Date of summary: Sat Sep 10 05:19:46 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 0.682 s +Fitted with method Port using 153 model solutions performed in 0.681 s Weighting: none diff --git a/inst/web/vignettes/FOCUS_D.html b/inst/web/vignettes/FOCUS_D.html index d02b1b61..9e19315d 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: 0.9.44.9000 ## R version: 3.3.1 -## Date of fit: Sat Sep 10 04:13:37 2016 -## Date of summary: Sat Sep 10 04:13:38 2016 +## Date of fit: Sat Sep 10 05:20:59 2016 +## Date of summary: Sat Sep 10 05:20:59 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 0.643 s +## Fitted with method Port using 153 model solutions performed in 0.641 s ## ## Weighting: none ## diff --git a/inst/web/vignettes/FOCUS_L.html b/inst/web/vignettes/FOCUS_L.html index 10d1eb01..8223a3f4 100644 --- a/inst/web/vignettes/FOCUS_L.html +++ b/inst/web/vignettes/FOCUS_L.html @@ -236,15 +236,15 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> summary(m.L1.SFO)</code></pre> <pre><code>## mkin version: 0.9.44.9000 ## R version: 3.3.1 -## Date of fit: Sat Sep 10 04:13:38 2016 -## Date of summary: Sat Sep 10 04:13:38 2016 +## Date of fit: Sat Sep 10 05:20:59 2016 +## Date of summary: Sat Sep 10 05:20:59 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.086 s +## Fitted with method Port using 37 model solutions performed in 0.09 s ## ## Weighting: none ## @@ -329,8 +329,8 @@ summary(m.L1.SFO)</code></pre> <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> <pre><code>## mkin version: 0.9.44.9000 ## R version: 3.3.1 -## Date of fit: Sat Sep 10 04:13:39 2016 -## Date of summary: Sat Sep 10 04:13:39 2016 +## Date of fit: Sat Sep 10 05:21:00 2016 +## Date of summary: Sat Sep 10 05:21:00 2016 ## ## ## Warning: Optimisation by method Port did not converge. @@ -342,7 +342,7 @@ summary(m.L1.SFO)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 188 model solutions performed in 0.443 s +## Fitted with method Port using 188 model solutions performed in 0.441 s ## ## Weighting: none ## @@ -426,8 +426,8 @@ plot(m.L2.FOMC, show_residuals = TRUE, <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> <pre><code>## mkin version: 0.9.44.9000 ## R version: 3.3.1 -## Date of fit: Sat Sep 10 04:13:40 2016 -## Date of summary: Sat Sep 10 04:13:40 2016 +## Date of fit: Sat Sep 10 05:21:01 2016 +## Date of summary: Sat Sep 10 05:21:01 2016 ## ## Equations: ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -496,8 +496,8 @@ 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: 0.9.44.9000 ## R version: 3.3.1 -## Date of fit: Sat Sep 10 04:13:41 2016 -## Date of summary: Sat Sep 10 04:13:41 2016 +## Date of fit: Sat Sep 10 05:21:02 2016 +## Date of summary: Sat Sep 10 05:21:02 2016 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -506,7 +506,7 @@ 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 0.797 s +## Fitted with method Port using 336 model solutions performed in 0.899 s ## ## Weighting: none ## @@ -585,8 +585,8 @@ plot(mm.L3)</code></pre> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> <pre><code>## mkin version: 0.9.44.9000 ## R version: 3.3.1 -## Date of fit: Sat Sep 10 04:13:42 2016 -## Date of summary: Sat Sep 10 04:13:42 2016 +## Date of fit: Sat Sep 10 05:21:03 2016 +## Date of summary: Sat Sep 10 05:21:03 2016 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -595,7 +595,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 137 model solutions performed in 0.326 s +## Fitted with method Port using 137 model solutions performed in 0.403 s ## ## Weighting: none ## @@ -685,15 +685,15 @@ plot(mm.L4)</code></pre> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> <pre><code>## mkin version: 0.9.44.9000 ## R version: 3.3.1 -## Date of fit: Sat Sep 10 04:13:42 2016 -## Date of summary: Sat Sep 10 04:13:42 2016 +## Date of fit: Sat Sep 10 05:21:04 2016 +## Date of summary: Sat Sep 10 05:21:04 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.106 s +## Fitted with method Port using 46 model solutions performed in 0.109 s ## ## Weighting: none ## @@ -745,15 +745,15 @@ plot(mm.L4)</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> <pre><code>## mkin version: 0.9.44.9000 ## R version: 3.3.1 -## Date of fit: Sat Sep 10 04:13:42 2016 -## Date of summary: Sat Sep 10 04:13:43 2016 +## Date of fit: Sat Sep 10 05:21:04 2016 +## Date of summary: Sat Sep 10 05:21:04 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 0.15 s +## Fitted with method Port using 66 model solutions performed in 0.152 s ## ## Weighting: none ## diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf Binary files differindex 27b9951d..85e50e65 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 004a808c..212b1abb 100644 --- a/inst/web/vignettes/compiled_models.html +++ b/inst/web/vignettes/compiled_models.html @@ -251,21 +251,21 @@ mb.1 <- microbenchmark( print(mb.1)</code></pre> <pre><code>## Unit: milliseconds ## expr min lq mean median uq -## deSolve, not compiled 6507.8296 6549.5160 6597.4319 6591.2024 6642.2330 -## Eigenvalue based 890.5249 917.6589 928.4907 944.7928 947.4735 -## deSolve, compiled 735.4908 742.0363 749.3996 748.5817 756.3540 +## deSolve, not compiled 6410.2240 6437.0229 6461.3866 6463.8218 6486.9680 +## Eigenvalue based 887.5697 915.3026 929.6279 943.0355 950.6570 +## deSolve, compiled 737.4060 745.6645 749.1956 753.9229 755.0903 ## max neval cld -## 6693.2636 3 c -## 950.1543 3 b -## 764.1264 3 a</code></pre> +## 6510.1142 3 c +## 958.2786 3 b +## 756.2578 3 a</code></pre> <pre class="r"><code>autoplot(mb.1)</code></pre> -<p><img src="data:image/png;base64,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" width="672" /></p> -<p>We see that using the compiled model is by a factor of 8.8 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 src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAJACAMAAAB7fzyHAAAC8VBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQWFhYXFxcZGRkaGhobGxscHBwdHR0eHh4fHx8gICAiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVHR0dISEhJSUlKSkpLS0tNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7///9b+1OZAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO3de4AU1b3g8TLGxJiH8eZmb/Ym2WQ3d+/m3s3dHIneTUzM5umeZmAYkfcbBlGBKAENqCjgExEFJCgCqxAT4gNU1sQH8YGKrhoU5ekjIAhOgqA8BGa6/tqq6u6Zrt8M4+ma6eqec76fP6iu6jqnqp3MN9XT3TOeDwBIxKv0CQBAV0VAASAhAgoACRFQAEiIgAJAQgQUABIioACQEAEFgIQIKAAkREABICECCgAJEVAASIiAAkBCBBQAEiKg5vY1dC3v+/6BSp8DYBcRBQJqjoACrhNRIKDmCCjgOhEFAmqOgAKuE1EgoOYIKOA6EQUCao6AAq4TUSCg5ggo4DoRBQJqjoACrhNRIKDmCCjgOhEFAmqOgAKuE1EgoOYIKOA6EQUCao6AAq4TUSCg5ggo4DoRBQJqjoACrhNRIKDmCCjgOhEFAmqOgAKuE1EgoOYIKOA6EQUCao6AAq4TUSCg5ggo4DoRBQJqjoACrhNRIKDmCCjgOhEFAmqOgAKuE1EgoOYIKOA6EQUCao6AAq4TUSCg5ggo4DoRBQJqjoACrhNRIKDmCCjgOhEFAmqOgAKuE1EgoOYIKOA6EQUCao6AAq4TUSCg5pIGdOeGTv0KGiOgQGcTUSCg5hIGdMtZalrnfg0NEVCgs4koEFBzCQN6nVLdXu3cL6IZAgp0NhEFAmouYUDPVkr9tnO/iGYIKNDZRBQIqLlkAd15WhDQ/9PJX0UjBBTobCIKBNRcsoA+rwgoYAsRBQJqLllAf09AAWuIKBBQc8kCejMBBawhokBAzSUL6CUEFLCGiAIBNZcsoCMIKGANEQUCai5ZQLsTUMAaIgoE1FyygH6XgALWEFEgoOYSBfQNRUABa4goEFBziQL6PAEF7CGiQEDNJQroQwQUsIeIAgE1lyigy5Q6k4AClhBRIKDmEgX0ZqXqCChgCREFAmouUUCvUGo0AQUsIaJAQM0lCug4paYQUMASIgoE1FyigA5U6kYCClhCRIGAmksUUK26LSGggCVEFAiouUQB/a766R0EFLCEiAIBNZckoNuU6ktAAVuIKBBQc0kC+melLiCggC1EFAiouSQBfUypKwgoYAsRBQJqLklAlys1j4ACthBRIKDmkgT010otJ6CALUQUCKi5JAGdrtSfCChgCxEFAmqutID+dvaW4N9fKPUqAQVsIaJAQM2VFNA1SvXY2dAwSKkGAgrYQkSBgJorKaD3K6WWNjScpU5rIqCALUQUCKi5kgM6omH3aUr7BBSwhYgCATVXckBP374hqCgBBawhokBAzZUcUPXQaqWmEFDAGiIKBNRc6QG97jdK/ZqAAtYQUSCg5koP6MDrlXqQgALWEFEgoOZKD+jpY5R6lYAC1hBRIKDmSg+oOk11O0BAAWuIKBBQc6UG9AdhQnv4BBSwhogCATVXakB/GQb0cgIK2ENEgYCaKzWgswcHAX2UgAL2EFEgoOZKDujabqrPUQIK2ENEgYCaKzmg/sb7P/AJKGAPEQUCaq70gOYQUMAWIgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnopBSQHdq3XrjpmvPrxswYf5uw93L49iHanUPAQVcJypRuYBm52rdfVhfrWvuMtm9DLTeSUABlEBUonIBfUDXPXTE9/fcqvXTBruXAQEFUBpRicoFdJRenbsxV08z2L0MVqzYT0ABlEBUomIBPaD1/tytdXrIR+9eNgQUgDFRibIHdN30YbXjH9mRi9G2mefVDpn+Ynhzv9abcns07c+VdMPV9b0umLkxvBm2a56+Ldq8Tff+MD5UeuO6c2vPXfBeG5M03jm2buytB/bcUF878s7DfvisfcfD42qHzVjn+/Gn8LHpY+fcgoACrhPxKXdAl2V0ZkhGXx3F6IEaXVdfp/XC8J6xetCjh4r2vCuja4YHez/k56q2Xg/Jhtvv1HPl0LhVNbrniBo9YE/rSaZOWrmopx7ff2K4mO+HzbxSX3r71O6Z3/ixgMamj51z6LWcXe+VYFUsoHeWMrSzBP+/dKgSxwXsJepT5oBu0Pr2Q/7+WTqM0Vs1fZ/N+tm1/fRTwV1bB2hdO+Xurdncnpt05rdH/EO36F5/y1UtOyS6RM2OCBfxoTHbajL3Nfr7puprWk8yvcn3V2o9LVis0AP9sJmZcIKXe+ktxQGNTR8751CTylldyiN/PBbQe5L+BwRQxcoc0Mlh1oIKjg9jdEP+5fY1+rJwsfeeCzNBpPrdHl46+lP0jdGeE8Mrxahqi6PrwU16TLbV0GIz9Jxw8X7NOU2tJnk9WHmjeeGHzbw6GrRYTy0OaGz62DmHCCiANpU3oNk++rXoxiNhjMbqEfWh4bp//v79z8wfqvXg7cHNfvkfia7WE/JVe1MPC9K5QN/ntzW02cD8wLe2HG01yVG/eOGHzcxdwb6uBxQHtHj6+DlHj2JizroPS/BILKC/K2VoZzni+42VOC5gL5Gf8gZ0r9bvRzc2hjE6Rxf0bNkl+8JQPTm4gNT6g/ye/fNVy47Rm/2mgTV7/WMMjRwoHMJvcxKx0HprYdTBooAWTx8/52K8iAS4TvSnvAFtrtvmMEZjdNHRp099O39rg8582NK+zbp3oXbL9SL/z3pGuDU2NCbo3YHC7bYmkQHNXaK+p/W+ooAWTx8/52IEFHCd6E+ZfwbaP/90eHUYoyv1S9HKh7vf8/2h+o/5ffZHxeqbT9uf9IWF2u0KnsPP1s+HW2NDY4Jn3K9HN7a8sL+tSWRAH4t2WKf7Fj+Fj00fO+diBBRwnehPmQN6ee5Fm+yEMEYr8x85mqeX+v4cPTT/1PtJPdwPX7qJepOdpOc1R2+C3tB7UGN4KzY0brJeEC6O9sscaHMSEdAJ0av+U/WU4oDGpo+dczECCrhO5KfMAQ2eBi885B+cE70l6PBQveiw37gqU7sruLys08Meercxu/fB3jp8jXqjziw/Gr4Dqfbd5ug9qM/XS6J5YkP9FSsaWw6xUdfc3+gfmq0vbXsSEVA956B/cL7WG4sDGps+ds7FCCjgOlG4cr+RfpHWmaEZvTSK0Ys9dc3Ic3RmTXjPqyOCQnXvGfwzO7ooXKx1jxEZXfuo3xy9vd213pGbJzZU68NFh7grGDSqVvd7t+1JREBnROeTWebH3kgfmz52zkUIKOA6EbhyBzT79PThtePXNn9gcmCPkddvz93V+NjUMXV9xs3ckt917VX1tefNjF5aKnwM/XI9qTBR8dB4QP3XZoysHbNg3zEmEQHd8ejYnkOnvZybpvijnC3Tx8+5BQEFXCcC1yV/I/3R6J2dSUTNTIqAAq4TUeiSAX2nV9KRBBRAB4godMmAzpj90fu0jYAC6AARhS4Z0MeTPoMnoAA6QkShSwY0OQIKoANEFBwLaIcQUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUXMkBPfDLmhU+AQXsIaJAQM2VHNBZSn3nbQIK2ENEgYCaKzWg15+p1Km3EVDAHiIKBNRcqQHtE/4hpXoCCthDRIGAmis1oKeGAf1+loAC1hBRIKDmSg1o4CyldhBQwBoiCgTUXOkB/dllSq0hoIA1RBQIqLnSA3rRAqV+Q0ABa4goEFBzpQd0SfDvdQQUsIaIAgE1V3pAX3peqfEEFLCGiAIBNVdyQH/esE2pPgQUsIaIAgE1V3JAr2hoOFP9kIAC1hBRIKDmSgrog0p1+38NDb2UOkhAAVuIKBBQcyUF9I3M6fODRb1SbxFQwBYiCgTUXEkBbdi9I/x3slLPEVDAFiIKBNRcaQHNmaXUgwQUsIWIAgE1lySgS5RaQkABW4goEFBzSQL6gFIzCShgCxEFAmouSUCfVmoSAQVsIaJAQM0lCehGpUYQUMAWIgoE1FySgO7qpnoQUMAWIgoE1FySgDb8SJ1BQAFbiCgQUHOJAlqn1AICClhCRIGAmksU0FFKzSCggCVEFAiouUQBvVipXxBQwBIiCgTUXKKAXqfUQAIKWEJEgYCaSxTQhdFfliOggBVEFAiouUQBvU+p0wgoYAkRBQJqLlFAH4/+vDEBBawgokBAzSUK6HoCCthDRIGAmksU0He6EVDAGiIKBNRcooA2/ISAAtYQUSCg5pIFtC8BBawhokBAzSUL6AUEFLCGiAIBNZcsoDMIKGANEQUCai5ZQG8noIA1RBQIqLlkAf0jAQWsIaJAQM0lC+hmAgpYQ0SBgJpLFtCGHwUBvaNzv4hmCCjQ2UQUCKi5hAENX4b/Y+d+Ec0QUKCziSgQUHMJA7pCqZ+/07lfRDMEFOhsIgoE1FzCgDbcPvH5zvwKGiOgQGcTUSCg5pIGtFIIKNDZRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKg5Agq4TkSBgJojoIDrRBQIqDkCCrhORIGAmiOggOtEFAioOQIKuE5EgYCaI6CA60QUCKi5qg3oxoWTL7724d1yMwEFOpuIAgE1V6UB3TSxmwplVok7CCjQ2UQUCKi56gzovWeqvO9sjN9DQIHOJqJAQM1VZUBvOlWp7177zCt3/2+lno3fRUCBziaiQEDNVWFAd18SXHmO3hWe3SUEFCg7EQUCaq76ArpzbNDPm5uisyOgQPmJKBBQc1UX0B2jleq2PH92BBQoPxEFAmqu2gL61mClvrOqcHYEFCg/EQUCaq6iAd1xy/W3rNhSvOXlWqX+fXXz2RFQoPxEFAiouYoG9IbwjUqn9p63Pr/+7rLvK3XG2pazI6BA+YkoEFBzFQ3or/Jv9uw2aP4zOxs2L+8b3D5rU9HZEVCg/EQU0gmobnFTZ825U2ujI+/stANUOqAzp2dyET0199Gj8/cUnx0BBcpPVCKtgJ7TN2+B36GqtXAvoC/5/sbZZxU+d6RXZWNnR0CB8hOVSCugO9tZTcbJgPp+0yu/HluXGTjt8UZxugQUKD/xbVeRgK5Ysb/jczoa0GMioED5iW+7igS0UxDQOAIKlJ/4tqvkU/it146svej5fKe2zTyvdsj0F8ObwZbGpYNr65cd8v15+rZoyDbd+0Pfb3x00sAegyY93OTn+1aIXH7GoklaDvX4+NohVzwfrRWP93fNP6+uz3kLcmcWG7lu+rDa8Y/sIKAA4sS3XQUDuqpG1wzP6KVRpx6o0XX1dVov9KMqzhl7z9Le+lrfX6+HRK+V3Knn+mFOdf9wr/l+mwEtnqT5UHN1ZkhG5zpcPH5rne4xapDWdVvkyGWZaMjVBBRAnPi2Syugw+vzNvj53L1dk7nriH8oaFrQqbdq+j6b9bNr++mnoiqOCy4+n9GZg352iA7f7JgdES7e1j2ez/pH79I9DrcV0NgkzUfWsw/4B+Zr/bIYP0nPPBAEulZfIkZu0Pr2Q/7+WboQ0OzonBeOVNBUg4Cujw9p9P2mypwsYCvxbZf6+0D/7Odzd7W+Mbwr+8uwUzfop6Md1+jLoiqGBTwU7bY4uircpMcEF6IvXhhdIR6O7mgd0NgkzUe+KLqCna4vFuPr9OZwZeX8RWLkZH1NdGLjCwFtyr9xaHWZ/usYudIgoBtTOhcAOZV7Cj9I5z5H81jYqbF6RHR9Olz3j6rY0Lzbm3pYUMAF+r7m0Y1rjxHQ2CTNh1odLdfrc7Lx8eP0pJcKbwUqHpnto1+LNj5CQAG0q2IBPaD1B9HKxrBT5zRfofaMqtjUPCo7JrhSbBpYszfckt340IJLztbHCGhskuZDRdeZ/vta742Pf2OU1n2n3rUl7GrxyL1av99yYpF9OXv+WkGTDQK6Nj4keBgHK3OygK3Et13FArqvENAtYafG6KIfzsZfGVquF/l/1jPCDe9eoDPn3rByQ6uAHom2xCZpPlTuOjfI4sH4eL/xuQUXdNf6wnfiIw8UArqZF5EAxIlvu8o9he+XT9sTYaeu1Lk6fLj7PRnQXcFz+Nk6ehvSZfrKvwWLplYB3RFtiU3SfKjHouXLerAYHzn09AQ9SYzsn38Kv5qAAogT33aVC+jlena0Mjns1Eo9LVqZp5e2enPnBL2h96Dop5W9dPiWI//VWED3BVvujbbEJmk+1IXRzz6v0FeL8ePOi15Q26zPFiMvD3cNnu5PIKAA4sS3XeUCujWT+d1R//DC7mGnDg/Viw77jasytbtaBfRBfb5eEm2oj15K2jAsel4e7XW4Ri/J+lsHRrvGJmk+VP5tTDVvi/ETwp39fdeEV6CxkcFT94WH/INzdHUGNLvl3nk3LXzykPxvTECB8hPfdqn/Nqa+hwtlvDuja0bU9F2t64KVF3vqmpHn6Mwav1VA9waJ3RFtWKX16IuH6+mjdb/78nvN07p+bGZZbtfiSZqPfFv0rviaVXL8uoyuqx/RXffeIkcu0joztPAO/yJVENDsumt+lntLwBnXiC8lAQXKT6Qt9feB6uaA+uunDam7dNsrelS4sm3mwB4jr98e3hQBDZ5TT8pNk31iQp/+Ux/Lbj2v7q78Xkd+f27tkLuzzR/lbJ6k+cg7nxpXO+KaLa3G+xtnDO/Z94Ilu305Mvv09OG149dW3WfhV6+ZpVWL798dOzsCCpSfSFvlfyP9o4U8Vr1KBzT/K+kHzly+avG404Kbl3xYdHYEFCg/EYXKBfS23svCRXainlOxcyhNRQM6NZfPAQvzf1hu/fnB2qCi30lPQIHyE1GoXEDX6rpns/7BBVq/UrFzKE1FA/rigNoBExevL9qy5HSlanc3nx0BBcpPRKFyAc3O1rp3+HuS5lXsFEpUbX8X/okzldJvF86OgALlJ6JQwZ+BZp+6ZFBt/eWPZT961+pQbQFteP4nSv3s9fzZEVCg/EQUKv8iUtdRdQFtWHeWUj/M/xZoAgqUn4gCATVXfQFteDWj1Om/ja7hCShQfiIKBNRcFQa0YesApdSI55r8vw0loEDZiSgQUHPVGNCGnROjzyX9/NTg3xfidxFQoLOJKBBQc1UZ0IaG5T/JvUX01MvEHQQU6GwiCgTUXJUGtGH74uE/OrP71OfkdgIKdDYRBQJqrloDeiwEFOhsIgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBVwnokBAzRFQwHUiCgTUHAEFXCeiQEDNEVDAdSIKBNQcAQVcJ6JAQM0RUMB1IgoE1BwBBezy3LT7ShwhokBAzRFQwCobzlDq3tKGiCgQUHMEFLDKdKVUv9KGiCgQUHMEFLDJuz8NAtrtjZLGiCgQUHMEFLDJ8yr0cEljRBQIqDkCCthkoVI/UmphSWNEFAioOQIK2GSsUrOUmlrSGBEFAmqOgAIW2X2m+vcNSo0uaZCIAgE1R0ABizys1LkHlaoraZCIAgE1R0ABe+warNTv/R+oH5Y0SkSBgJojoIA9blHqxwf87qpbSaNEFAioOQIK2OMspZ7w/R4ENC0EFLDGeqX6+wQ0RQQUsMb9Sl3nE9AUEVDAGnOVutsnoCkioIA1Zin1B5+ApoiAAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtYgoGkjoIA1CGjaCChgDQKaNgIKWIOApo2AAtaoioDu1Lr1xk3Xnl83YML83Ya7V9axT6nVPQQUsEa1BjQ7V+vuw/pqXXOXye5JaL2z06YhoICLqjWgD+i6h474/p5btX7aYPckCKgRAgocU7UGdJRenbsxV08z2D2JTgroihX7CSjgpioN6AGt9+durdNDPnr3RDopoBECCriowgFdN31Y7fhHduQis23mebVDpr8Y3tyv9abcHk37cyXdcHV9rwtmbiw0aZ6+Ldq8Tff+MD60Vb8alw6urV92SMwyU4eKXqJ647pza89d8F4bB2u8c2zd2FsP7LmhvnbknYf9ML07Hh5XO2zGOt+PP4WPnUbssbUgoIA1KhvQZRmdGZLRV0eReaBG19XXab0wvGesHvTooaI978romuHB3g/lmuav10Oy4fY79Vw5NCbYd87Ye5b21teKWR6eq/W1c99v3nFVje45okYP2NP6YFMnrVzUU4/vPzFczI+aeaW+9Pap3TO/8WMBjZ1G7LEFsg/n/OX9ruWg7x+u9DkAVerm5oCWNKxzArpB69sP+ftn6TAyb9X0fTbrZ9f2008Fd20doHXtlLu3ZnN7btKZ3x7xD92ie/0tV6vskOgSNTsiXMSHyoCOC0L8jM4cFLPEn8Jvq8nc1+jvm6qvaX2w6U2+v1LracFihR4YjcyEB3q5l95SHNDYacQeW6hJ5axO9t8KQPX5dXNAOzJLwoBODnMVVHB8GJkb8i+3r9GXhYu991yYCeLT7/bwktCfom+M9pwYXgFGtVocXedt0mOyrYYWC/YNW3coylxslnhAZ+g54eL9mnOaWh3s9WDljeZFNPLqaNBiPbU4oLHTiD22EAEFrFPJgGb76NeiG4+EkRmrR9SHhuv++fv3PzN/qNaDtwc3++V/JLpaT8jX6k09LEjnAn2f39bQZsG+0c8boszFZokHdGD+rre2HG11sKN+8SIambvSfV0PKA5o8WnEH1v0aBfnbNnftXzo+0cqfQ5AlZrTHNCShnVKQPdqnftZwMYwMufogp4tu2RfGKonBxeGWn+Q37N/vlbZMXqz3zSwZq9/jKHNAW0qBDQ+SyygBwqn4rd5MLHQemth1MGigBafRvyxFeNFJMAalXwRqblam8PIjNFFs06f+nb+1gad+bClaZt170LFlutF/p/1jHBrbGhc4V1EsYBGs8QCGvTuQOF2WweTAc1dor6n9b6igBafRvyxFSOggDUq+ip8//zT3NVhZK7UL0UrH+5+z/eH6j/m99kflahvPll/0hcWKrYreA4/Wz8fbo0NjSsOaHyWWECDZ9yvRze2vLC/rYPJgD4W7bBO9y1+Ch87jdhjK0ZAAWtUNKCX516MyU4II7My/5GjeXqp78/RQ/NPqZ/Uw/3wJZnZ0Z6T9LzmmE3QG3oPagxvxYbGxQIamyX+M9DJekG4ONovc6DNg4mATojeHTBVTykOaOw0Yo+tGAEFrFHRgAZPbxce8g/Oid7qc3ioXnTYb1yVqd0VXF7W6WEPvduY3ftgb32PH/4kMbP8aPjOotp3m2P2oD5fL4nmiQ31V6xoPEZAY7MEmza27LdR19zf6B+arS9t+2AioHrOQf/g/GiGloDGTiP22AgoYKXKvpF+kdaZoRm9NIrMiz11zchzdGZNeM+rI4LydO8Z/DM7uthbrHWPERld+2hLzPZ213pHbp7YUK0PHyOgsVmCJ9kDLtnTvONdweZRtbrfu20fTAR0RnTemWV+7I30sdOIPTYCCtiosgHNPj19eO34tc0fhBzYY+T123N3NT42dUxdn3Ezt+R3XTPbg3UAAAsMSURBVHtVfe15M98ujuLlelJhouKh7QS0eBb/2foeA//asudrM0bWjlmw7xgHEwHd8ejYnkOnvdw8cxuPIP7YWhBQwBpV8ctEOtfR6B2b5dShX0NCQAFrWBjQd3qV+wgEFEDIwoDOmF3uIxBQACELA/p4uZ/BE1AAEQsDWn4EFECIgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWIKBpI6CANQho2ggoYA0CmjYCCliDgKaNgALWuFmpFT4BTREBBaxxr1I3+gQ0RQQUsMZLSg3zCWiKCChgjXd/rLq9TEBTREABe1ynlD7s1xDQtBBQwB5v91LqQf+H6sySRokoEFBzBBSwyP1Kjf/wVNWzpEEiCgTUHAEFLLLze+qM15UaVdIgEQUCao6AAjapV+rXSk0paYyIAgE1R0ABm8xT6iylFpQ0RkSBgJojoIBN1qjQQyWNEVEgoOYIKGCT3f8rDOjmksaIKBBQcwQUsMqUoJ+1pQ0RUSCg5ggoYJUXT1dqSWlDRBQIqDkCCtjlD2Pnv1vaCBEFAmqOgAKuE1EgoOYIKOA6EQUCao6AAq4TUSCg5ggo4DoRBQJqjoACrhNRIKDmCCjgOhEFAmqOgAKuE1EgoOYIKOA6EQUCao6AAq4TUSCg5ggo4DoRBQJqjoACrhNRIKDmCCjgOhEFAmqOgAKuE1EgoOYIKOA6EQUCao6AAq4TUSCg5ggo4DoRBQJqjoACrhNRIKDmCCjgOhEFAmqOgAKuE1EgoOYIKOA6EQUCaq6rBfTNNWs2VfocALuIKBBQez2i1LxKnwNgNQJqLwIKlBkBtRcBBcqMgNqLgAJlRkDtRUCBMiOg9iKgQJkRUHsRUKDMCCgAJERAASAhAgoACRFQAEiIgAJAQgQUABIioACQEAG1xb16Z/7WrhsH1Qya9c5HrQDoKAJqicOjCwHderbWg7Su29L+CoAOI6BWaNxwmc4HtLFeT3nP3zNZj2psbwVAxxFQGyyp0boQ0DW6/6FgcbCffrq9FQAdR0Bt8MhNN91UCOiNem60nKNnt7cCoOMIqC0KAZ2kn4yWT+hL2lsB0HEE1BaFgI7SL0fLdXp0eysAOo6A2qIQ0Dr9ZrR8U/dubwVAxxFQW7QE9K1o+YaubW8FQMcRUFu0fgo/sr0VAB1HQG3R8iLSU9HySX1xeysAOo6A2qIQ0Fl6frScr29sbwVAxxFQWxQC+pQedCRYHB0Uvl3+2CsAOo6A2qL5o5yj9HVH/aPX6vrG9lYAdBwBtUUhoP6WOl13YS/d+/X2VwB0GAG1RXNA/XdmDawZeOPuj1oB0FEEFAASIqAAkBABBYCECCgAJERAASAhAgoACRFQAEiIgAJAQgQUABIioACQEAEFgIQIKAAkREDRtfzIi/tq8qmi8cpgt38qWpRZW0f5dHieaRwcJSOg6FraCmjCvHxUQOPlTKejBLRrIaDoWlbeEvmi5+Vu3Ol3IKBf2759V3v3V0dA396+nYBWKQKKLumfvKL/6SYOaPvDqiWg5T8qkiKg6JJiAf3gg/1J5vioKuWnjZcz4bE6eE4EtEoRUHRJsYAmZFilir+IlNrBUTICii4pFtD8SliZu39w8ie+MniL/86Ifzjhq5ln8ztsOu+fP/25fx39UnyOQpWONa5o2pZF4cDbLvqfX/rk13+8+MgxDrH5/H/+zMmnXlD4OyvPDP7ap076+pBnW468aeiXP/EfvzfvQG7Lw2d/6YR/6LuucBQ5OwGtUgQUXVLbAf3GBO8fv3ey5510x5eO+x//xfOO+1O4PXvD8fmX7H9xtHiOloC2Pa69gM77eH7Kb+5t6xDZ6/PrJ60IV4+eW3jTwOgj+bn+8OmPf/vbJ3jet/YF60cG5+795IrcUcTsBLRqEVB0SW0H9LhPLfX9934QlOcLa/3sRZ733XD7rZ73+YnL7hh3kueNKp6jJaBtj2snoH88zjvx3KUrZ/1Xz+vb1iHmet7xIxfdNfZE7+Twb6BeGKyOWrykPujihbm5Tvlcr6CcW7/peRcF6+cHpzDw1vm9vM9ER5GzE9CqRUDRJbUdUO/acPlscGNhsPzgeO+zwWLPZ7xvNYTbt/9nz/tT0RwtAW1zXHsBDa4Y7wvX3vsP3mebWh/inRO9T0dHWnNCGMjXPO/Tj4erTwaB3JA74hnZcMOjnvcd33/Z8z6xKly9NfeGTzG7T0CrFgFFl3SMgH4QLg943scOhTe+Em2f7Xmbcvs9E78ELQpoW+PaC6jyvL9GY5dfddX7rQ8x1fN+mVvv7Z3q++M87+Lc6sTcJWhwxMf8/CGDWc/zvAty9/80Whez+wS0ahFQdEltB/QrufXCBzxz27t7/5bfr+kU778VzdES0DbHtRfQPp7X85WWmeQhvud5b+bW/7L6Cd//N8/7S271TS8MavhDg0NF5/Cvnrc1t7oyWhez+wS0ahFQdEnHehXeL76R2/6t4k9+frFojuJX4dsY115AN58czPWtCb/blhsoD/H33ieaig70ee+T+dWmE7wvRHP9Y/E5fK559y3RupjdJ6BVi4CiSyohoF8vrtsni+boQED9nef+XTTfv1zxQRuHOL5wTZtzfMuvPPmKd3zxEXO3WnY/mLsnPrtPQKsWAUWXVEJAv+39vO05OhJQ32987praLwWR+/I7rQ9xsndStmi16Ar0k97n/VYBPcX7RGNu9fXCPcWz+wS0ahFQdEklBPRs78ttz9GxgIaya7p73vDWh/jvnpd/B/22O+7YX/Qz0L8ET839VgE91fO25Fb/b3EpC7P7BLRqEVB0SSUEdJ7nPZ7b/sxXv3pz0RwdCGi/bw7JDflb9Avx5CHGed7lufVfeqc0+WM971e51Um519tFQC9sfhW+e7QuZvcJaNUioOiSSgjo3s9439geru75tvexvxTN0YGADvFOeC3a9Xee99PWh9hwnPfZZ8L1LZ/3evr+es/7zJpw9fGTPO9Vv1VAXyu8D3RZ7n2gYnafgFYtAoouqYSA+guCfk1Yes+VX/G8KcVzdCCgd3je3/9q+YOLB33c8+5u4xCXeN7HRy+++9K/804KUzguXF2yZNTxuQ8eyYD648NPIt12a1/vG9G6nJ2AVi0Cii6plIBmrzou9/r4xyYUv7TTkYA29im85v7xq9o6RONF+bs/+7tw9cjIwu5jjsaOmL91ZEju3i+8HK3L2Qlo1SKg6JJKCajvvzLi6yee0m3U5vgcHXoR6cm6fznlxK99f9o7ftuHeGH41z91yuljC7/w/qmB/+nET31tcNFvY4rferjuSyd8sf/rhXU5OwGtUgQUzupCVepCp+oWAgpndaEqdaFTdQsBhbO6UJW60Km6hYDCWV2oSl3oVN1CQOGs6HXudv4ufLXg78JXLwIKZxFQdBQBBYCECCgAJERAASAhAgoACRFQAEiIgAJAQgQUABIioACQEAEFgIQIKAAkREABICECCgAJEVAASIiAAkBC/x8JwCUZNrvJZgAAAABJRU5ErkJggg==" width="672" /></p> +<p>We see that using the compiled model is by a factor of 8.6 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> <pre class="r"><code>rownames(smb.1) <- smb.1$expr smb.1["median"]/smb.1["deSolve, compiled", "median"]</code></pre> <pre><code>## median -## deSolve, not compiled 8.804921 -## Eigenvalue based 1.262110 +## deSolve, not compiled 8.573584 +## Eigenvalue based 1.250838 ## deSolve, compiled 1.000000</code></pre> </div> <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level2"> @@ -286,18 +286,18 @@ smb.1["median"]/smb.1["deSolve, compiled", "median" <pre class="r"><code>smb.2 <- summary(mb.2) print(mb.2)</code></pre> <pre><code>## Unit: seconds -## expr min lq mean median uq -## deSolve, not compiled 13.741831 13.74759 13.815509 13.753350 13.852348 -## deSolve, compiled 1.358402 1.35862 1.368666 1.358838 1.373798 +## expr min lq mean median uq +## deSolve, not compiled 13.370040 13.424534 13.501075 13.479027 13.56659 +## deSolve, compiled 1.336599 1.336707 1.339399 1.336815 1.34080 ## max neval cld -## 13.951345 3 b -## 1.388759 3 a</code></pre> +## 13.654158 3 b +## 1.344784 3 a</code></pre> <pre class="r"><code>smb.2["median"]/smb.2["deSolve, compiled", "median"]</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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KAeAqqMbUBHuO73Ol4goEA9BFQZ24C2uu6cjo4WAgrUQUCVsQ3ot1z3Rx0dvzzplN+ldTKB/CKgytgG9ETXXVK89vqmlE4lkGcEVBnbgBb9S1qnEcg7AqpMgoAuT+s0AnlHQJVJENAn0zqNQN4RUGUSBPSFtE4jkHcEVJkEAV2f1mkE8o6AKpMgoFvSOo1A3hFQZewDenJaZxHIPQKqjH1AT0/rLAK5R0CVsQ9oa1pnEcg9AqqMfUCHpHUWgdwjoMrYB3RCWmcRyD0Cqox9QC9I6ywCuUdAlbEP6OVpnUUg9wioMvYBvTqlkwjkHwFVxj6gN6R1FoHcI6DK2Af0trTOIpB7BFQZ+4D+PK2zCOQeAVXGPqAPp3UWgdwjoMrYB/TZtM4ikHsEVBn7gL6U1lkEco+AKmMf0M1pnUUg9wioMtYB7ZfWSQTyj4AqYx1Q/jQ7YJ8IqDLWAW1P6yQC+UdAlbEO6Ni0TiKQfwRUGeuAXpjWSQTyj4AqYx3QK9M6iUD+EVBlrAO6OK2TCOQfAVXGOqD8KjywTwRUGeuA/jalcwj0AQRUGduA9tuS1kkE8o+AKmMV0BWuOz6tcwj0AQRUGauAvjv/uy+ndQ6BPoCAKmMVUAD/IQKqDAEF5BBQZQgoIIeAKkNAATkEVBkCCsghoMoQUEAOAVWGgAJyCKgyBBSQQ0CVIaCAHAKqDAEF5BBQZQgoIIeAKkNAATkEVBkCCsghoMoQUEAOAVWGgAJyCKgyBBSQQ0CVIaCAHAKqDAEF5BBQZQgoIIeAKkNAATkEVBkCCsghoMoQUEAOAVWGgAJyCKgyBBSQQ0CVIaCAHAKqDAEF5BBQZQgoIIeAKkNAATkEVBkCCsghoMoQUEAOAVWGgAJyCKgyBBSQQ0CVIaCAHAKqDAEF5BBQZQgoIIeAKkNAATkEVBkCCsghoMoQUEAOAVWGgAJyCKgyBBSQQ0CVIaCAHAKqDAEF5BBQZQgoIIeAKkNAATkEVBkCCsghoMoQUEAOAVWGgAJyCKgyBBSQQ0CVIaCAHAKqDAEF5DQ7oPe3zE4wFYkCAY2PgAJymh3QM1z3efupSBQIaHwEFJDT7ICe6ro/t5+KRIGAxkdAATkZCOhN9lORKBDQ+AgoICcDAb3efioSBQIaHwEF5GQgoDPtpyJRIKDxEVBATgYC+l37qUgUCGh8BBSQk4GAnmc/FYkCAY2PgAJyMhDQcfZTkSgQ0PgIKCAnAwEdaj8ViQIBjY+AAnIyENB2+6lIFAhofAQUkJOBgBbspyJRIKDxEVBATgYCerr9VCQKBDQ+AgrIyUBAv2k/FYkCAY2PgAJyMhDQfvZTkSgQ0PgIKCAnAwF137GeikSBgMZHQAE5WQjoZuupSBQIaHwEFJCThYBusJ6KRIGAxkdAATlZCOhq66lIFAhofAQUkJOFgL5gPRWJAgGNj4ACcrIQ0H+1nopEgYDGR0ABOVkI6JPWU5EoEND4CCggJwsBfdR6KhIFAhofAQXkZCGgy62nIlEgoPERUEBOFgJ6v/VUJAoEND4CCsjJQkDvtp6KRIGAxkdAATlZCOid1lORKBDQ+AgoICcLAf1n66lIFAhofAQUkJOFgP7YeioSBQIaHwEF5GQhoAutpyJRIKDxEVBAThYCOs96KhIFAhofAQXkZCGgV1tPRaJAQOMjoICcLAR0tvVUJAoEND4CCsjJQkBnWE9FokBA4yOggJwsBPRS66lIFAhofAQUkJOFgF5oPRWJAgGNj4ACcrIQ0MnWU5EoEND4CCggJwsBnWg9FYkCAY2PgAJyshDQsdZTkSgQ0PgIKCAnCwEdYT0ViQIBjY+AAnKyENBB1lORKBDQ+AgoICcLAR1gPRWJAgGNj4ACcrIQ0P7WU5EoEND4CCggJwsB/bb1VCQKBDQ+AgrIyUJAT7OeikSBgMZHQAE5WQjo31hPRaJAQOMjoICcLAS0n/VUJAoEND4CCsjJQkDdd2ynIlEgoPERUEBOJgL6xsZ71llNRaJAQOMjoICcTAR0w1lum9VUJAoEND4CCsjJREAfc90TrKYiUSCg8RFQQE4mArqYgDYMAQXkZCKglxLQhiGggJxMBHQUAW0YAgrIyURAv01AG4aAAnIyEdC/JqANQ0ABOZkIqEtAG4aAAnIIqDIEFJBDQJUhoIAcAqoMAQXkEFBlCCggh4AqQ0ABOQRUGQIKyCGgyhBQQA4BVYaAAnIIqDIEFJDT/ID+NQFtJAIKyGl+QE87noA2EAEF5DQ/oH97KgFtIAIKyMlAQPsT0AYioICcDAR0JAFtIAIKyMlAQC8hoA1EQAE5GQjoPxPQBiKggJwMBLRz2oTTCGijEFBATgYCWvx/9RkEtFEIKCCHgCpDQAE5BFQZAgrIIaDKEFBADgFVhoACcgioMgQUkENAlSGggBwCqgwBBeQQUGUIKCCHgCpDQAE5BFQZAgrIIaDKEFBADgFVhoACcgioMgQUkENAlSGggBwCqgwBBeQQUM/bYkztjWuuOrd96LRF78Tcvbn2fUg19xBQQA4BrZuf7gXG9B89yJiWO+PsnoQxW8SWIaBAUxDQuvn5hWlfXvxPs/VGY56KsXsSBBTIPQJaNz/jzYrSlQVmZozdkxAK6LJlnQQUaBICWi8/243pLF1bZUZ+8u6JCAU0QECBptAd0FWzRrdNffjNUmQ2zT2nbeSs3/tXO41ZU9pjb2eppKvnTBhw3txXKk1aaH4c3LzJDPw4PFrTr66lI9om3LEzsspc46v6iGrj1RPbJi5+v86Ddd0+uX3yjdu3Xjuhbdztuzw/vW8+NKVt9OxVnhd+Cx86jNBz60VAATmqA3pHwRRGFsycIDK/aDHtE9qNucm/Z7IZ/sjOqj3vLJiWMcW9l5ea5v3BjOz2b7/dLIiOhhT3nT/5nqUDzVWRVR5aYMxVCz7s2fHBFtM6tsUM3Vr7YDMuvu+WVjN1yEX+xaKgmVeYy26e0b/wEy8U0NBhhJ6b742Sjq0ApBQDuq2Zj98TUKspmYCuNubmnV7ndcaPzGstg57p9rqfHWyeLN61fqgxbdPvXt9d2nONKfx0t7fzBjPgvVKtukcGL1G7x/oX4dFoQKcUQ/y0KeyIrBJ+C7+ppXBvl/fBDPOD2gebtdfz7jNmZvFimRkWTBb8B3pxgFlXHdDQYYSem2+vW7Ii2X8rABn0zUpA92eRhAG91M9VsYJT/chcW/64faX5vn+x7Z7zC8X4DL45iPV0c32w50X+K8CgVkuC13lrzKTumtFqxX391u0MMhdaJRzQ2Wa+f/Fhy5l7ax5sQ3FjY89FMDknGFpiZlQHNHQYoefmI6BA39PEgHafZV4OrjzsR2ayGTvBN8YMKd/f+fSiUcaM2Fy8Orj8I9EVZlq5Vq+a0cV0Ljb3evVGexT3DX7eEGQutEo4oMPKd722bk/Ng+3xqi+CydIr3Q1maHVAqw8j/NyCZ3t2yXO7AUgpVmBPMx+/J6BWUyIB3WZM6WeQr/iROdNUtPbu0v3cKHNp8YWhMR+V9xxSrlX3JLPW2zusZZu3j9GegO6tBDS8Siig2yuH4tV9sMiFMesrUzuqAlp9GOHnVo0PkQA5ij9E6qnWWj8yk0zVqrNmvFG+ttoUPu5t2lozsFKxu8wt3gtmtn9raDSs8i2iUECDVUIBLfZue+V6vQeLBrT0EvV9Yz6oCmj1YYSfWzUCCshRHFBvSPlt7go/MleY54ONj9953/NGmV+X9+kMSjSonKzHzPmVir1dfA8/z/zWvzU0GlYd0PAqoYAW33FvCK6se66z3oNFA/posMMqM6j6LXzoMELPrRoBBeRoDujlpQ9juqf5kbmv/CtHC81Sz5tvRpXfUj9hxnj+RzLzgj0vNgt7YjbNrB44vMu/FhoNCwU0tEr4Z6CXmsX+xZ7Bhe11HywS0GnBtwNmmOnVAQ0dRui5VSOggBzNAS2+vb1pp7djfvBVn12jzC27vK4HC21vF19etpvRy9/t6t72wEBzj+f/JLFw1x7/m0Vt7/bE7AFzrrk1WCc06i1b1rWPgIZWKd70Su9+r5iW+7u8nfPMZfUfLBJQM3+Ht2NRsEJvQEOHEXpuBBRIh+aAercYUxhVMEuDyPy+1bSMO9MUVvr3vDS2WJ7+rcV/zQte7C0x5oyxBdP2SG/MtvU35s3SOqFRY3btI6ChVYpvsode0vuF1juLN49vM4Pfrf9gkYDODo67cIcX+iJ96DBCz42AAqlQHdDup2aNaZv6bM8vQg47Y9w1m0t3dT06Y1L7WVPmrivv+uyVE9rOmftGdRQvNxdXFqoe/Q8CWr2K98yEM4b9e++eL88e1zZp8Qf7eLBIQN98ZHLrqJkv9qxc5xmEn1svAgrIUR3QVOwJvrGZpv36Y0gIKCCHgEp7a0Daj0BAgYwgoNJmz0v7EQgokBEEVNrjab+DJ6BAVhDQ/CGgQEYQUGUIKCCHgCpDQAE5BFQZAgrIIaDKEFBADgFVhoACcgioMgQUkENAlSGggBwCqgwBBeQQUGUIKCCHgCpDQAE5BFQZAgrIIaDKEFBAThYC+t5bBLRhCCggJwMBXXPyCScR0EYhoICcDAT0ereIgDYIAQXkZCCgUwhoAxFQQE4GAjqIgDYQAQXkZCCgf0dAG4iAAnIyENCTCGgDEVBATvMDeqpLQBuIgAJymh/QEwhoIxFQQE7zA+oS0EYioIAcAqoMAQXkEFBlCCggh4AqQ0ABOQRUGQIKyCGgyhBQQA4BVYaAAnIIqDIEFJBDQJUhoIAcAqoMAQXkZCKg3yCgDUNAATmZCGgbAW0YAgrIyURAJxHQhiGggJxMBHQ2AW0YAgrIyURAf0JAG4aAAnIyEdDnT3T7WU1FokBA4yOggJxMBPS16/rNtJqKRIGAxkdAATmZCOgW26lIFAhofAQUkJOFgJ7wru1UJAoEND4CCsjJQkBPsp6KRIGAxkdAATlZCOg3raciUSCg8RFQQE4WAvr/rKciUSCg8RFQQE4WAvr31lORKBDQ+AgoICcLAW21nopEgYDGR0ABOVkI6HespyJRIKDxEVBAThYCOtR6KhIFAhofAQXkZCGgo6ynIlEgoPERUEBOFgI63noqEgUCGh8BBeRkIaDnWE9FokBA4yOggJwsBPQfrKciUSCg8RFQQE4WAnqx9VQkCgQ0PgIKyMlCQC+znopEgYDGR0ABOVkI6D9aT0WiQEDjI6CAnCwEdI71VCQKBDQ+AgrIyUJA51pPRaJAQOMjoICcLAT0R9ZTkSgQ0PgIKCAnCwH9J+upSBQIaHwEFJCThYDeZD0ViQIBjY+AAnKyENCl1lORKBDQ+AgoICcLAf2Z9VQkCgQ0PgIKyMlCQO+1nopEgYDGR0ABOVkI6APWU5EoEND4CCggJwsB/bX1VCQKBDQ+AgrIyUJAH7eeikSBgMZHQAE5WQjoU9ZTkSgQ0PgIKCAnCwH9nfVUJAoEND4CCsjJQkD/YD0ViQIBjY+AAnKyENA11lORKBDQ+AgoICcLAX3VeioSBQIaHwEF5GQhoFuspyJRIKDxEVBATgYCeoL9VCQKBDQ+AgrIyUBAT7afikSBgMZHQAE5GQjo/7WfikSBgMZHQAE5GQjot+2nIlEgoPERUEBOBgLaaj8ViQIBjY+AAnIyENCz7KciUSCg8RFQQE4GAjrSfioSBQIaHwEF5GQgoBPtpyJRIKDxEVBATgYCer79VCQKBDQ+AgrIyUBAp9tPRaJAQOMjoICcDAT0B/ZTkSgQ0PgIKCAnAwFdYD8ViQIBjY+AAnIyEFD7vxaegCZHQAE5zQ7o/3HdX9lPRaJAQOMjoICcZgd0vHvyRvupSBQIaHwEFJDT7ICu++FvEkxFokBA4yOggJxmBzSZSBQIaHwEFJBDQJUhoIAcAqoMAQXkEFBlCCggh4AqQ0ABOQRUGQIKyCGgyhBQQA4BVYaAAnIIqDIEFJBDQJUhoIAcAqoMAQXkEFBlCCggh4AqQ0ABOQRUGQIKyCGgyhBQQA4BVYaAAnIIqDIEFJBDQJUhoIAcAqoMAQXkEFBlCCggh4AqQ0ABOQRUGQIKyCGgyhBQQA4BVYaAAnIIqDIEFJBDQJUhoIAcAqoMAQXkEFBlCCggh4AqQ0ABOQRUGQIKyHlh5cq3m30M9iJRIKAAmmGi677d7GPYbwQUQDMQUABIiIACQEIEFAASIqAAkBABBYCECCgAaEZAASAhAgoACRFQAEiIgAJAQgQUABIioACQEAEF0GA/N1vK196+fnjL8OveaurR7A8CCqCxdp1dCej67xgz3Jj2dc09oOQIKIBG6lr9fVMOaNcEM/19b+ulZnxXkw8qKQIKoIFubTGmEtCVZsjO4sWOweap5h5UYgQUQAM9/MMf/rAS0OvNguByvpnXzEPaDwQUQINVAnqxeSK4/I25pJmHsx8IKIAGqwR0vHkxuFxlzm7m4ewHAgqgwSoBbTevBpevmoFNPJr9QUABNFhvQF8LLjeatmYezn4goAAarPYt/LhmHs5+IKAAGqz3Q6Qng8snzHebeTj7gYACaLBKQK8zi4LLReb6Zh7OfiCgABqsEtAnzfDdxYs9w/kiPQDE0/OrnOPN1Xu8PVeZCfwqJwDEUgmot67dtJ8/wAzc0NzjSY6AAmiwnoB6b103rGXY9e809Wj2BwEFgIQIKAAkREABICECCgAJEVAASIiAAkBCBBQAEiKgAJAQAQWAhAgoACREQAEgIQIKAAkRUPQVpzlhX0q+VDDvyh1a7fp/VnPbEf5j1t6MTCOg6CvqBTRhkggo4iGg6CvuuyHwBccpXbnd24+Afnnz5reFjy+0fu1hvbF5MwHNHQKKPubPnKr/UScOaLol28f6BDR3CCj6mFBAP/qoM8kaBBTxEFD0MaGAJkRAEQ8BRR8TCmh5wy/T3acedcixI9Z5b439k4O/VHimvMOac/78iM/+5dnPh9foKdmmC/73MYce960lu/ex+9pz//zIo44/r/IXVDw94sufPvy4kc/0rrJm1BcP+c/fWLi9dMtD3znm4D8ZtKqyfnR1Apo7BBR9TP2AfmWa86ffOMpxDr/tmNAcSUcAAATJSURBVAP+139znAMe82/vvvbA8kf2/7Cneo1KyRYeVL77q9vq7d59TXn78GX+5p6JlS8AnL27vMqvjjjo618/2HG+9kFxe/eI0r2HLiutH1mdgOYQAUUfUz+gB3x6qee9f2qxVp9/1uu+wHFO9m+/0XE+d9Edt0053HHGV69RLtmvD3AOm7j0vuv+u+MMqrf7Asc5cNwtd04+zDnK/2slzy9ujl9y64RiF88vrXL0ZwcUy7n+q45zQXH73OJhDLtx0QDnyGD96OoENIcIKPqY+gF1rvIvnyleual4+dGBzmeKF1uPdL7W4d+++b86zmNVa5RLVnzFeK9/+f5/cj6zt3b3tw5zjgimVh7sB/JlxznicX/ziWIgV5ce9W+6/RsecZwTPe9FxznkQX/zxtIXPiOrewQ0hwgo+ph9BPQj/3K743xqp3/l2OD2eY6zprTf0+GXoOWSuY7z78H2XVde+WHt7jMc58LS9kDneM+b4jjfLW1eVHoJWnzUR73ywxbXO8dxzivdf3qwHVndI6A5REDRx9QP6LGl7coveJZu7+/8VXm/vUc7/6NqjXLJznKc1n/rvTW6+zcc59XS9usrfuN5f+U4r5c2X3X8oPo/ONhZtd5fOs760uZ9wXZkdY+A5hABRR+zr0/hveorpdu/Vv2bn1+oWqO829qjird/bdrPNpVuje7+x84he6uGPuccWt7ce7Dz+WCVP61e77M9u68LtiOrewQ0hwgo+hiLgB5XXcRDq9ao7LZl4h8F9/3FP35UZ/cDK69rSw7s/eNLjnUOrH7U0rXe3XeU7gmv7hHQHCKg6GMsAvp15+/qr9Fbsq5//UHbMcXIffGt2t2Pcg7vrtqsegV6qPM5ryagRzuHdJU2N1TuqV7dI6A5REDRx1gE9DvOF+uvES5Z98r+jjOmdvf/6Tjlb9Bvuu22zqqfgb5efGvu1QT0eMdZV9r8ZfX6ldVrHhY5QEDRx1gEdKHjPF66/ekvfelHVWuUdxv81ZGl7feCP9wuuvsUx7m8tH2hc/Reb7LjfK+0eXHp8/ZIQM/v+RS+f7AdWd0joDlEQNHHWAR025HOVzb7m1u/7nzq9ao1yruNdA5+Odj+meOcXrv76gOczzztb6/7nNPqeX9wnCNX+puPH+44L3k1AX258j3QO0rfA42s7hHQHCKg6GMsAuotLjZv2tJ7rjjWcaZXr1He7TbH+ePv3fXAkuEHOc7ddXa/xHEOOnvJ3Zf9kXO4n8Ip/uatt44/sPSLR9GAelP930T68Y2DnK8E29HVCWgOEVD0MTYB7b7ygNJn6p+aVv1xUGW3rrMqn7kfdGW93bsuKN/9mZ/5m7vHVXaftCf0qOVru0eW7v38i8F2dHUCmkMEFH2MTUA979/GHnfY0SeMXxteo2f/J9r/4ujDvnzKzLe8+rs/N+a4Tx/db3LlD69/cth/OezTXx7xTGSVnmsPtR9z8BeGbKhsR1cnoLlDQIEaTSoZAc0dAgrUIKCIh4ACNQgo4iGgQA0CingIKFAj+Gw8xb8Xvh7+Xvg8IqBADQKKeAgoACREQAEgIQIKAAkRUABIiIACQEIEFAASIqAAkBABBYCECCgAJERAASAhAgoACRFQAEiIgAJAQgQUABL6/zCLQzIOWDvBAAAAAElFTkSuQmCC" 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" width="672" /></p> <p>Here we get a performance benefit of a factor of 10.1 using the version of the differential equation model compiled from C code!</p> <p>This vignette was built with mkin 0.9.44.9000 on</p> <pre><code>## R version 3.3.1 (2016-06-21) |