rstan_windows_install.R

### Before running this script make sure you have 
### RTools installed:
### https://github.com/stan-dev/rstan/wiki/Install-Rtools-for-Windows

# your version of R
sessionInfo()$R.version

# your version of RTools; If your Rtools version is
# at another location, specify the correct location
# in the file.path()
rtools_path <- file.path("C:/Rtools/VERSION.txt")
(rtools <- readLines(rtools_path))

# Pull out major and minor version of RTools and R
rtools_v <- as.integer(unlist(strsplit(gsub("[^0-9]", "", rtools), "")))[1:2]
r_minor <- as.integer(unlist(strsplit(gsub("[^0-9]", "", sessionInfo()$R.version$minor), "")))
r_v <- c(as.integer(sessionInfo()$R.version$major), 
         r_minor[1], r_minor[2])

# R must be version 3.0.2 or later
r_less_than_302 <- as.integer(paste0(r_v, collapse = "")) < 302
if (r_less_than_302)
  stop("Please, install the latest R version")

# If you have installed R version 3.2.x or lower, your
# R tools versions must match your R version
r_less_than_330 <- as.integer(paste0(r_v, collapse = "")) <= 329
if (r_less_than_330) 
  if (rtools_v[1] != r_v[1] || rtools_v[2] != r_v[2])
    stop("There is a mismatch between your R version and your RTools version. Please, upgrade.")

# Check PATH for presence of RTools and gcc
sys_path <- Sys.getenv("PATH")
if (!grepl("Rtools", sys_path))
  stop("Rtools is missing from your PATH")

if (!grepl("gcc", sys_path))
  stop("gcc is missing from your PATH")

# check gcc version
system('g++ -v')

# Set C++ compiler options
dotR <- file.path(Sys.getenv("HOME"), ".R")
if (!file.exists(dotR)) dir.create(dotR)
(M <- file.path(dotR, "Makevars"))

if (!file.exists(M)) file.create(M)
cat("\nCXXFLAGS=-O3 -mtune=native -march=native -Wno-unused-variable -Wno-unused-function", 
    file = M, sep = "\n", append = TRUE)

cat('Sys.setenv(BINPREF = "C:/Rtools/mingw_$(WIN)/bin/")',
    file = file.path(Sys.getenv("HOME"), ".Rprofile"), 
    sep = "\n", append = TRUE)

# to get rid of deprecation warnings on new compilers
cat("\nCXXFLAGS += -Wno-ignored-attributes -Wno-deprecated-declarations", 
    file = M, sep = "\n", append = TRUE)

cat(readLines(M), sep = "\n")

# change to number of processors on your system
# here we have 2, thus: "-j2"
Sys.setenv(MAKEFLAGS = "-j2") 

# install RStan from CRAN
install.packages('rstan', repos = 'https://cloud.r-project.org/', dependencies = TRUE)

# test the install with a small model
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

scode <- "
parameters {

  real y[2]; 
} 
model {
  y[1] ~ normal(0, 1);
  y[2] ~ double_exponential(0, 2);
} 
"

fit1 <- stan(model_code = scode, iter = 10, verbose = FALSE) 
print(fit1)

### the print(fit1) line should produce something like:

# Inference for Stan model: 5687625e16aea608e5f18bd8a8d65730.
# 4 chains, each with iter=10; warmup=5; thin=1; 
# post-warmup draws per chain=5, total post-warmup draws=20.
# 
# mean se_mean   sd  2.5%   25%   50%   75% 97.5% n_eff Rhat
# y[1] -0.04    0.30 1.34 -2.69 -0.87  0.05  1.10  1.68    20 0.82
# y[2]  0.28    0.67 3.01 -5.52 -0.90 -0.25  2.36  5.87    20 1.36
# lp__ -1.91    0.31 1.29 -4.58 -2.29 -1.73 -0.95 -0.36    18 1.10
# 
# Samples were drawn using NUTS(diag_e) at Tue Aug 16 14:07:56 2016.
# For each parameter, n_eff is a crude measure of effective sample size,
# and Rhat is the potential scale reduction factor on split chains (at 
# convergence, Rhat=1).