diff --git a/articles/examples.html b/articles/examples.html index 97162b9..e499b8a 100644 --- a/articles/examples.html +++ b/articles/examples.html @@ -758,9 +758,9 @@
tic(); eval <- evaluate_design(poped.db); toc()
-#> Elapsed time: 2.76 seconds.
+#> Elapsed time: 2.806 seconds.
tic(); eval <- evaluate_design(poped.db.Rcpp); toc()
-#> Elapsed time: 1.187 seconds.
The difference is noticeable and gets larger for more complex ODE models.
diff --git a/articles/handling_LOQ.html b/articles/handling_LOQ.html index 1577580..88b8264 100644 --- a/articles/handling_LOQ.html +++ b/articles/handling_LOQ.html @@ -283,8 +283,8 @@The D2 method is the same as removing the last design point, as you can se below.
diff --git a/articles/intro-poped.html b/articles/intro-poped.html index a20b965..189e8b2 100644 --- a/articles/intro-poped.html +++ b/articles/intro-poped.html @@ -432,7 +432,7 @@Design optimization#> d_CL 0.0625 28 26 #> sig_prop 0.04 14 15 #> -#> Total running time: 16.882 seconds +#> Total running time: 16.844 seconds plot_model_prediction(output$poped.db)
We see that there are four distinct sample times for this design. @@ -493,7 +493,7 @@
Here we see that the optimization ran somewhat quicker, but gave a @@ -558,7 +558,7 @@
We see that the optimal doses are 31.6 and 55.2 for the two groups. This leads to population trough concentrations of 0.2 and 0.35 for the two groups of patients at 240 hours:
diff --git a/pkgdown.yml b/pkgdown.yml index 418d8d5..c3813a4 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -6,7 +6,7 @@ articles: articles/handling_LOQ: handling_LOQ.html intro-poped: intro-poped.html articles/model_def_other_pkgs: model_def_other_pkgs.html -last_built: 2024-10-08T07:34Z +last_built: 2024-10-08T07:41Z urls: reference: https://andrewhooker.github.io/PopED/reference article: https://andrewhooker.github.io/PopED/articles diff --git a/reference/Doptim.html b/reference/Doptim.html index 6f0bfb2..70dcc81 100644 --- a/reference/Doptim.html +++ b/reference/Doptim.html @@ -329,7 +329,7 @@