1 files changed, 14 insertions, 21 deletions
diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd
index bac284c5..b6d54710 100644
--- a/vignettes/compiled_models.Rmd
+++ b/vignettes/compiled_models.Rmd
@@ -15,22 +15,20 @@ output:
 ```{r, include = FALSE}
 library(knitr)
 opts_chunk$set(tidy = FALSE, cache = TRUE)
-if (!require("ccSolve")) 
-  message("Please install the ccSolve package for this vignette to produce sensible output")
-
 ```
 
 # Benchmark for a model that can also be solved with Eigenvalues
 
 This evaluation is taken from the example section of mkinfit. When using an mkin version
-greater than 0.9-36 and the ccSolve package is installed and functional, you will get a
-message that the model is being compiled when defining a model using mkinmod.
+greater or equal than 0.9-36 and the C++ compiler g++ is installed and functional (on Windows,
+install Rtools), you will get a message that the model is being compiled when
+defining a model using mkinmod.
 
 ```{r create_SFO_SFO}
 library("mkin")
 SFO_SFO <- mkinmod(
   parent = list(type = "SFO", to = "m1", sink = TRUE),
-  m1 = list(type = "SFO"))
+  m1 = list(type = "SFO"), odeintr_compile = "yes")
 ```
 
 We can compare the performance of the Eigenvalue based solution against the
@@ -39,28 +37,23 @@ the microbenchmark package.
 
 
 ```{r benchmark_SFO_SFO, echo=-(1:2)}
-# Redefining the model, in order not to confuse the knitr cache which leads to segfaults
-suppressMessages(SFO_SFO <- mkinmod(
-  parent = list(type = "SFO", to = "m1", sink = TRUE),
-  m1 = list(type = "SFO")))
 library("microbenchmark")
 mb.1 <- microbenchmark(
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", use_compiled = FALSE, 
-          quiet = TRUE),
-  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),
   mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE),
+  mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),
   times = 3, control = list(warmup = 1))
 smb.1 <- summary(mb.1)[-1]
-rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "deSolve, compiled")
+rownames(smb.1) <- c("deSolve, not compiled", "Eigenvalue based", "odeintr, compiled")
 print(smb.1)
 ```
 
-We see that using the compiled model is almost a factor of 8 faster than using the R version
+We see that using the compiled model is more than a factor of 7 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:
 
 ```{r}
-smb.1["median"]/smb.1["deSolve, compiled", "median"]
+smb.1["median"]/smb.1["odeintr, compiled", "median"]
 ```
 
 # Benchmark for a model that can not be solved with Eigenvalues
@@ -73,15 +66,15 @@ FOMC_SFO <- mkinmod(
   m1 = list(type = "SFO"))
 
 mb.2 <- microbenchmark(
-  mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE),
-  mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE),
+  mkinfit(FOMC_SFO, FOCUS_2006_D, solution_type = "odeintr", quiet = TRUE),
   times = 3, control = list(warmup = 1))
 smb.2 <- summary(mb.2)[-1]
-rownames(smb.2) <- c("deSolve, not compiled", "deSolve, compiled")
+rownames(smb.2) <- c("deSolve, not compiled", "odeintr, compiled")
 print(smb.2)
-smb.2["median"]/smb.2["deSolve, compiled", "median"]
+smb.2["median"]/smb.2["odeintr, compiled", "median"]
 
 ```
 
 Here we get a performance benefit of more than a factor of 8 using the version
-of the differential equation model compiled from C code using the ccSolve package!
+of the differential equation model compiled from C++ code using the odeintr package!