BwUniCluster2.0/Software/R/Rstan
General information
rstan
provides the R interface for Stan, a platform for statistical modeling and high-performance statistical computation.
To be compatible with our R installation, we recommend to install the development version of rstan.
Installation instructions
Please enter the following code, presented in the boxes below, directly in your shell/command line on bwUniCluster2.0.
Preparations
Installing glmnet
involves compiling source code. Therefore, ensure that the following flags are set in $HOME/.R/Makevars
:
cat ~/.R/Makevars CXX14=g++ CXX17=g++ CXX14FLAGS += -std=c++14 CXX17FLAGS += -std=c++17 CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option
If necessary, the appropriate compiler flags can be set by running the following lines of code:
mkdir -p ~/.R echo "CXX14=g++" > ~/.R/Makevars echo "CXX17=g++" >> ~/.R/Makevars echo "CXXFLAGS = -O3 -fPIC -march=cascadelake -ffp-contract=off -fno-fast-math -fno-signed-zeros -fopenmp -Wno-unknown-warning-option" >> ~/.R/Makevars echo "CXX14FLAGS += -std=c++14" >> ~/.R/Makevars echo "CXX17FLAGS += -std=c++17" >> ~/.R/Makevars
Since installing glmnet
involves compiling, we start an interactive session on one of the compute nodes:
salloc -n 1 -t 30 -p dev_single
Within the interactive session, load the R 4.4.1 module and provide configuration information to install the R package v8
, rstan depends on.
# Load the R software module: module load math/R/4.4.1-mkl-2022.2.1-gnu-13.3 # Provide configuration information for the v8 R package export DOWNLOAD_STATIC_LIBV8=1
Installing the R package(s) [takes roughly 20 min]
Now, start R
and install RStan
using the lines of code below:
R -q > install.packages("remotes") > install.packages("RcppParallel") > install.packages("V8") > remotes::install_github("hsbadr/rstan/StanHeaders@develop", force = TRUE) > remotes::install_github("hsbadr/rstan/rstan/rstan@develop", force = TRUE)
Testing the installation
To check whether the installation worked, make a test run in R
> library(rstan) > example(stan_model, package = "rstan", run.dontrun = TRUE)