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----
-title: "Performance benefit by using compiled model definitions in mkin"
-author: "Johannes Ranke"
-date: "`r Sys.Date()`"
-output:
-  html_document:
-    theme: united
-    toc: true
-    toc_float: true
-    mathjax: null
-vignette: >
-  %\VignetteIndexEntry{Performance benefit by using compiled model definitions in mkin}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include = FALSE}
-library(knitr)
-opts_chunk$set(tidy = FALSE, cache = FALSE)
-```
-
-## Model that can also be solved with Eigenvalues
-
-This evaluation is taken from the example section of mkinfit. When using an mkin version
-equal to or greater than 0.9-36 and a C compiler (gcc) is available, you will see
-a message that the model is being compiled from autogenerated C code when
-defining a model using mkinmod. The `mkinmod()` function checks for presence of
-the gcc compiler using
-
-```{r check_gcc}
-Sys.which("gcc")
-```
-First, we build a simple degradation model for a parent compound with one metabolite.
-
-```{r create_SFO_SFO}
-library("mkin")
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
-```
-
-We can compare the performance of the Eigenvalue based solution against the
-compiled version and the R implementation of the differential equations using
-the benchmark package.
-
-
-```{r benchmark_SFO_SFO, fig.height = 3}
-if (require(rbenchmark)) {
-  b.1 <- benchmark(
-    "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                      solution_type = "deSolve",
-                                      use_compiled = FALSE, quiet = TRUE),
-    "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                 solution_type = "eigen", quiet = TRUE),
-    "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
-                                  solution_type = "deSolve", quiet = TRUE),
-    replications = 3)
-  print(b.1)
-  factor_SFO_SFO <- round(b.1["1", "relative"])
-} else {
-  factor_SFO_SFO <- NA
-  print("R package benchmark is not available")
-}
-```
-
-We see that using the compiled model is by a factor of around 
-`r factor_SFO_SFO`
-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.
-
-
-## Model that can not be solved with Eigenvalues
-
-This evaluation is also taken from the example section of mkinfit.
-
-```{r benchmark_FOMC_SFO, fig.height = 3}
-if (require(rbenchmark)) {
-  FOMC_SFO <- mkinmod(
-    parent = mkinsub("FOMC", "m1"),
-    m1 = mkinsub( "SFO"))
-
-  b.2 <- benchmark(
-    "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D,
-                                      use_compiled = FALSE, quiet = TRUE),
-    "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
-    replications = 3)
-  print(b.2)
-  factor_FOMC_SFO <- round(b.2["1", "relative"])
-} else {
-  factor_FOMC_SFO <- NA
-  print("R package benchmark is not available")
-}
-```
-
-Here we get a performance benefit of a factor of
-`r factor_FOMC_SFO`
-using the version of the differential equation model compiled from C code!
-
-This vignette was built with mkin `r packageVersion("mkin")` on
-
-```{r sessionInfo, echo = FALSE}
-cat(capture.output(sessionInfo())[1:3], sep = "\n")
-if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {
-  cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))
-}
-```