Be more careful with vignettes use of parallelization

.Rbuildignore

@@ -19,3 +19,5 @@
 ^pkgdown$
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^doc$
+^Meta$

.gitignore

@@ -10,3 +10,5 @@ docs
 /vignettes/articles/RUNN/
 /vignettes/articles/mod-PowerPoint.pptx
 /vignettes/articles/mod-Word.docx
+/doc/
+/Meta/

vignettes/import-nonmem.Rmd

@@ -12,6 +12,11 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
+library(rxode2)
+setRxThreads(1L)
+library(data.table)
+setDTthreads(1L)
+
 ```
 
 The goal of `nonmem2rx` is to convert a NONMEM control stream to
@@ -39,11 +44,11 @@ control stream are:
 - The default NONMEM output extension; By default it is `.lst`.  You
   can set it to something else, like `.res`, using the following
   option: `options(nonmem2rx.lst=".res")`.
-  
+
 - Turn on [extended control
   stream](https://wfn.sourceforge.net/wfncs.htm#control_streams)
   support. You can turn it on by `options(nonmem2rx.extended=TRUE)`
-  
+
 You probably also want to change the name of parameters and
 compartments. The easiest way to name the parameters whatever you want
 is to pre-specify the names.  For example:
@@ -120,4 +125,3 @@ The import process steps are below:
 
 - Replaces the compartment names with the defined compartment names in
   the control stream (ie `COMP=(compartmenName)`)
-

vignettes/rxode2-validate.Rmd

@@ -12,6 +12,10 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
+library(rxode2)
+setRxThreads(1L)
+library(data.table)
+setDTthreads(1L)
 ```
 
 
@@ -37,21 +41,21 @@ generated outputs for the following items:
 - **Population Predictions:** this shows if the model translation is
   adequate to simulate general trends; This will validate structural
   model's population parameters coupled with the model structure.
-  
+
 - **Individual Predictions:** this shows if the model translation is
   able to replicate the same values over all the subjects within the
   modeling data-set.  This validates the model can reproduce the
-  between subject variability observed in the study. 
-  
+  between subject variability observed in the study.
+
 - **Individual Weighted Residuals:** this is one step further than the
   individual parameter validation, it couples the individual
   predictions, the observations and the residual specification to
   generate the individual weighted residuals. Since you can [modify
   the residual specification to create a nlmixr2-compatible
   model](https://nlmixr2.github.io/nonmem2rx/articles/convert-nlmixr2.html), this step is important to make sure
   the residual specification is the same.
-  
-  
+
+
 **Note:** the only part that is not validated with these three metrics
 is the between subject covariance matrix, `omega`. We assume this is
 correct as long as it is read in correctly.

vignettes/simulate-extra-items.Rmd

@@ -12,6 +12,10 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
+library(rxode2)
+setRxThreads(1L)
+library(data.table)
+setDTthreads(1L)
 ```
 
 This page shows a simple work-flow for directly simulating a different

vignettes/simulate-new-dosing-with-covs.Rmd

@@ -12,6 +12,10 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
+library(rxode2)
+setRxThreads(1L)
+library(data.table)
+setDTthreads(1L)
 ```
 
 ## Simulation with covariates or input parameters
@@ -31,7 +35,7 @@ In this case, we will use the Friberg myelosuppresion model originally
 contributed as an example by Yuan Xiong.
 
 With the simulated data in nlmixr2, babelmixr2, and some manual edits
-to simplify the model we run NONMEM 7.4.3.  
+to simplify the model we run NONMEM 7.4.3.
 
 Note in this case there are some PK parameters that are in the model
 that require some special handling to simulate with uncertainty or
@@ -108,7 +112,7 @@ plot(ci)
 ```
 
 This may be closer a constant theoretical dosing regimen you may wish
-to explore. 
+to explore.
 
 ## Option 3: simulate a larger study with a different condition (resampled PK parameters/covariates)
 

vignettes/simulate-new-dosing.Rmd

@@ -12,6 +12,10 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
+library(rxode2)
+setRxThreads(1L)
+library(data.table)
+setDTthreads(1L)
 ```
 
 This page shows a simple work-flow for directly simulating a different

vignettes/simulate-uncertainty.Rmd

@@ -12,6 +12,10 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
+library(rxode2)
+setRxThreads(1L)
+library(data.table)
+setDTthreads(1L)
 ```
 
 This page shows a simple work-flow for directly simulating a different
@@ -66,7 +70,7 @@ s
 
 ## Step 4: Summarize and plot
 
-Since there is a bunch of data, a confidence band of the simulation with uncertainty would be helpful. 
+Since there is a bunch of data, a confidence band of the simulation with uncertainty would be helpful.
 
 One way to do that is to select the interesting components, create a confidence interval and then plot the confidence bands: