Rstanarm: CRAN vs Development version installation issues

This is an awesome answer. I had the same difficulties with installing rstanarm as other people mentioned above and this fixed it. Could it be mentioned on the GitHub page for the survival branch - it would’ve saved many people a lot of time (around 8 hours in my case till I randomly found this reply)?

1 Like

@Frantisek_Bartos Yeah you are right – this should really be documented somewhere more obvious. And sorry it wasn’t at the time you went through this!

I’ve opened a PR to add this to the README, see here: https://github.com/stan-dev/rstanarm/pull/501. Hopefully @bgoodri and @jonah are happy to add this to the documentation, either via that PR or something similar.

4 Likes

Thanks a lot! Especially for developing these awesome features!

1 Like

I finally was able to install it (after months and trying for many hours) using:

install.packages("rstanarm", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

Thanks, @rok_cesnovar!

1 Like

If somebody reads this topic and his/her models do not run, causing R to crash, this thread provides a solution: Stan_surv crashes R repeatedly - #13 by binman

Everything is up-to-date, rstan models and non-survival models of rstanarm run; however, survival functionality of rstanarm is not working. Trying to to run a rstanarm survival model causes R to crash. Haven’t figure it out after 6 hours of trying.

Here’s what I did:

Reinstalled rstan according to the guidelines

https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started

Sample model runs well.

Installed the developer version:

install.packages("rstanarm", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

Installing package into ‘C:/Users/user/Documents/R/win-library/4.0’
(as ‘lib’ is unspecified)
trying URL ‘https://mc-stan.org/r-packages/bin/windows/contrib/4.0/rstanarm_2.21.2.zip
Content type ‘application/zip’ length 12617689 bytes (12.0 MB)
downloaded 12.0 MB

package ‘rstanarm’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in

  • C:\Users\Public\Documents\Wondershare\CreatorTemp\RtmpQ55DeO\downloaded_packages*
    >

Loading the package

library(rstanarm)

Loading required package: Rcpp
Registered S3 method overwritten by ‘htmlwidgets’:

  • method from *
  • print.htmlwidget tools:rstudio*
    This is rstanarm version 2.21.2
    - See Prior Distributions for rstanarm Models • rstanarm for changes to default priors!
    - Default priors may change, so it’s safest to specify priors, even if equivalent to the defaults.
    - For execution on a local, multicore CPU with excess RAM we recommend calling
  • options(mc.cores = parallel::detectCores())*

Trying to find stan_surv function
?stan_surv
No documentation for ‘stan_surv’ in specified packages and libraries:
you could try ‘??stan_surv’

However, I tried to run a stan_glm() model and it worked well.

Testing a survival model

#simulating data
library(simsurv)
set.seed(999111)
N <- 500
covs <- data.frame(id = 1:N,
trt = rbinom(N, 1L, 0.5))
dat = simsurv(dist = "weibull",
lambdas = 0.1,
gammas = 1.5,
betas = c(trt = -0.5),
tde = c(trt = 0.2),
x = covs,
maxt = 5)
dat = merge(dat, covs)
head(dat)

#model
mod3 <- stan_surv(
formula = Surv(eventtime, status) ~ tve(trt, degree = 0, knots = 4),
data = dat,
basehaz = "weibull",
chains = 3,
cores = 3,
seed = 1,
iter = 1000)

image

Rstudio log file: https://www.dropbox.com/s/pcocm29lr559jz5/rsession-user.log?dl=1

R.Version()

$platform
[1] “x86_64-w64-mingw32”
$arch
[1] “x86_64”
$os
[1] “mingw32”
$system
[1] “x86_64, mingw32”
$status
[1] “”
$major
[1] “4”
$minor
[1] “0.4”
$year
[1] “2021”
$month
[1] “02”
day [1] "15" svn rev
[1] “80002”
$language
[1] “R”
$version.string
[1] “R version 4.0.4 (2021-02-15)”
$nickname
[1] “Lost Library Book”

PC configuration

Intel(R) Core™ i5-8250U CPU @ 1.60GHz 1.80 GHz, 16GB of RAM, 64-bit OS, SSD HDD, Windows 10 Enterprise 20H2

session_info(pkgs = NULL, include_base = FALSE)
Packages ------------------------------------------------------------
 package      * version  date       lib source        
 assertthat     0.2.1    2019-03-21 [1] CRAN (R 4.0.4)
 base64enc      0.1-3    2015-07-28 [1] CRAN (R 4.0.3)
 bayesplot      1.8.0    2021-01-10 [1] CRAN (R 4.0.4)
 boot           1.3-27   2021-02-12 [1] CRAN (R 4.0.4)
 cachem         1.0.4    2021-02-13 [1] CRAN (R 4.0.4)
 callr          3.5.1    2020-10-13 [1] CRAN (R 4.0.4)
 cli            2.3.1    2021-02-23 [1] CRAN (R 4.0.4)
 codetools      0.2-18   2020-11-04 [2] CRAN (R 4.0.4)
 colorspace     2.0-0    2020-11-11 [1] CRAN (R 4.0.4)
 colourpicker   1.1.0    2020-09-14 [1] CRAN (R 4.0.4)
 crayon         1.4.1    2021-02-08 [1] CRAN (R 4.0.4)
 crosstalk      1.1.1    2021-01-12 [1] CRAN (R 4.0.4)
 curl           4.3      2019-12-02 [1] CRAN (R 4.0.4)
 DBI            1.1.1    2021-01-15 [1] CRAN (R 4.0.4)
 desc           1.3.0    2021-03-05 [1] CRAN (R 4.0.4)
 devtools     * 2.3.2    2020-09-18 [1] CRAN (R 4.0.4)
 digest         0.6.27   2020-10-24 [1] CRAN (R 4.0.4)
 dplyr          1.0.5    2021-03-05 [1] CRAN (R 4.0.4)
 DT             0.17     2021-01-06 [1] CRAN (R 4.0.4)
 dygraphs       1.1.1.6  2018-07-11 [1] CRAN (R 4.0.4)
 evaluate       0.14     2019-05-28 [1] CRAN (R 4.0.4)
 fansi          0.4.2    2021-01-15 [1] CRAN (R 4.0.4)
 fastmap        1.1.0    2021-01-25 [1] CRAN (R 4.0.4)
 fs             1.5.0    2020-07-31 [1] CRAN (R 4.0.4)
 generics       0.1.0    2020-10-31 [1] CRAN (R 4.0.4)
 ggplot2        3.3.3    2020-12-30 [1] CRAN (R 4.0.4)
 ggridges       0.5.3    2021-01-08 [1] CRAN (R 4.0.4)
 glue           1.4.2    2020-08-27 [1] CRAN (R 4.0.4)
 gridExtra      2.3      2017-09-09 [1] CRAN (R 4.0.4)
 gtable         0.3.0    2019-03-25 [1] CRAN (R 4.0.4)
 gtools         3.8.2    2020-03-31 [1] CRAN (R 4.0.3)
 htmltools      0.5.1.1  2021-01-22 [1] CRAN (R 4.0.4)
 htmlwidgets    1.5.3    2020-12-10 [1] CRAN (R 4.0.4)
 httpuv         1.5.5    2021-01-13 [1] CRAN (R 4.0.4)
 igraph         1.2.6    2020-10-06 [1] CRAN (R 4.0.4)
 inline         0.3.17   2020-12-01 [1] CRAN (R 4.0.4)
 jsonlite       1.7.2    2020-12-09 [1] CRAN (R 4.0.4)
 knitr          1.31     2021-01-27 [1] CRAN (R 4.0.4)
 later          1.1.0.1  2020-06-05 [1] CRAN (R 4.0.4)
 lattice        0.20-41  2020-04-02 [1] CRAN (R 4.0.4)
 lifecycle      1.0.0    2021-02-15 [1] CRAN (R 4.0.4)
 lme4           1.1-26   2020-12-01 [1] CRAN (R 4.0.4)
 loo            2.4.1    2020-12-09 [1] CRAN (R 4.0.4)
 magrittr       2.0.1    2020-11-17 [1] CRAN (R 4.0.4)
 markdown       1.1      2019-08-07 [1] CRAN (R 4.0.4)
 MASS           7.3-53.1 2021-02-12 [1] CRAN (R 4.0.4)
 Matrix         1.3-2    2021-01-06 [1] CRAN (R 4.0.4)
 matrixStats    0.58.0   2021-01-29 [1] CRAN (R 4.0.4)
 mime           0.10     2021-02-13 [1] CRAN (R 4.0.4)
 miniUI         0.1.1.1  2018-05-18 [1] CRAN (R 4.0.4)
 minqa          1.2.4    2014-10-09 [1] CRAN (R 4.0.4)
 munsell        0.5.0    2018-06-12 [1] CRAN (R 4.0.4)
 nlme           3.1-152  2021-02-04 [1] CRAN (R 4.0.4)
 nloptr         1.2.2.2  2020-07-02 [1] CRAN (R 4.0.4)
 pillar         1.5.1    2021-03-05 [1] CRAN (R 4.0.4)
 pkgbuild       1.2.0    2020-12-15 [1] CRAN (R 4.0.4)
 pkgconfig      2.0.3    2019-09-22 [1] CRAN (R 4.0.4)
 pkgload        1.2.0    2021-02-23 [1] CRAN (R 4.0.4)
 plyr           1.8.6    2020-03-03 [1] CRAN (R 4.0.4)
 prettyunits    1.1.1    2020-01-24 [1] CRAN (R 4.0.4)
 processx       3.4.5    2020-11-30 [1] CRAN (R 4.0.4)
 promises       1.2.0.1  2021-02-11 [1] CRAN (R 4.0.4)
 ps             1.6.0    2021-02-28 [1] CRAN (R 4.0.4)
 purrr          0.3.4    2020-04-17 [1] CRAN (R 4.0.4)
 R6             2.5.0    2020-10-28 [1] CRAN (R 4.0.4)
 Rcpp         * 1.0.6    2021-01-15 [1] CRAN (R 4.0.4)
 RcppParallel   5.0.3    2021-02-24 [1] CRAN (R 4.0.4)
 remotes        2.2.0    2020-07-21 [1] CRAN (R 4.0.4)
 reshape2       1.4.4    2020-04-09 [1] CRAN (R 4.0.4)
 rlang          0.4.10   2020-12-30 [1] CRAN (R 4.0.4)
 rmarkdown      2.7      2021-02-19 [1] CRAN (R 4.0.4)
 rprojroot      2.0.2    2020-11-15 [1] CRAN (R 4.0.4)
 rsconnect      0.8.16   2019-12-13 [1] CRAN (R 4.0.4)
 rstan          2.21.2   2020-07-27 [1] CRAN (R 4.0.4)
 rstanarm     * 2.21.2   2020-08-31 [1] local         
 rstantools     2.1.1    2020-07-06 [1] CRAN (R 4.0.4)
 sessioninfo    1.1.1    2018-11-05 [1] CRAN (R 4.0.4)
 shiny          1.6.0    2021-01-25 [1] CRAN (R 4.0.4)
 shinyjs        2.0.0    2020-09-09 [1] CRAN (R 4.0.4)
 shinystan      2.5.0    2018-05-01 [1] CRAN (R 4.0.4)
 shinythemes    1.2.0    2021-01-25 [1] CRAN (R 4.0.4)
 splines2       0.4.2    2021-02-21 [1] CRAN (R 4.0.4)
 StanHeaders    2.21.0-7 2020-12-17 [1] CRAN (R 4.0.4)
 statmod        1.4.35   2020-10-19 [1] CRAN (R 4.0.4)
 stringi        1.5.3    2020-09-09 [1] CRAN (R 4.0.3)
 stringr        1.4.0    2019-02-10 [1] CRAN (R 4.0.4)
 survival       3.2-10   2021-03-16 [1] CRAN (R 4.0.4)
 testthat       3.0.2    2021-02-14 [1] CRAN (R 4.0.4)
 threejs        0.3.3    2020-01-21 [1] CRAN (R 4.0.4)
 tibble         3.1.0    2021-02-25 [1] CRAN (R 4.0.4)
 tidyr          1.1.3    2021-03-03 [1] CRAN (R 4.0.4)
 tidyselect     1.1.0    2020-05-11 [1] CRAN (R 4.0.4)
 usethis      * 2.0.1    2021-02-10 [1] CRAN (R 4.0.4)
 utf8           1.2.1    2021-03-12 [1] CRAN (R 4.0.4)
 V8             3.4.0    2020-11-04 [1] CRAN (R 4.0.4)
 vctrs          0.3.6    2020-12-17 [1] CRAN (R 4.0.4)
 withr          2.4.1    2021-01-26 [1] CRAN (R 4.0.4)
 xfun           0.22     2021-03-11 [1] CRAN (R 4.0.4)
 xtable         1.8-4    2019-04-21 [1] CRAN (R 4.0.4)
 xts            0.12.1   2020-09-09 [1] CRAN (R 4.0.4)
 yaml           2.2.1    2020-02-01 [1] CRAN (R 4.0.3)
 zoo            1.8-9    2021-03-09 [1] CRAN (R 4.0.4)

Makevars.win

CXX14FLAGS += -mtune=native -O3 -mmmx -msse -msse2 -msse3 -mssse3 -msse4.1 -msse4.2

Makewars.win.bak includes

CXX14FLAGS=-O3 -mtune=native
CXX11FLAGS=-O3 -mtune=native

2 Likes

Glad that other post had the solution! I was about to suggest it may be related. The tve() function use b-splines under the hood.

1 Like

I am installing with the below command in my R which runs under Ubuntu:

install.packages(“rstanarm”, repos = c(“Repository for distributing (some) stan-dev R packages | r-packages”, getOption(“repos”)))

g++: internal compiler error: Killed (program cc1plus)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions.
/usr/lib/R/etc/Makeconf:175: recipe for target ‘stan_files/jm.o’ failed
make: *** [stan_files/jm.o] Error 4
make: *** Waiting for unfinished jobs…
g++: internal compiler error: Killed (program cc1plus)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions.
/usr/lib/R/etc/Makeconf:175: recipe for target ‘stan_files/mvmer.o’ failed
make: *** [stan_files/mvmer.o] Error 4
rm stan_files/jm.cc stan_files/continuous.cc stan_files/bernoulli.cc stan_files/ binomial.cc stan_files/lm.cc stan_files/polr.cc stan_files/surv.cc stan_files/co unt.cc stan_files/mvmer.cc
ERROR: compilation failed for package ‘rstanarm’

  • removing ‘/usr/local/lib/R/site-library/rstanarm’

The downloaded source packages are in
‘/tmp/RtmpbwEBgC/downloaded_packages’
Warning message:
In install.packages(“rstanarm”, repos = c(“Repository for distributing (some) stan-dev R packages | r-packages”, :
installation of package ‘rstanarm’ had non-zero exit status

This error can occur when your computer runs out of memory during compilation. As part of the installation rstanarm compiles several stan models, each requiring ~3gb of RAM. If you have specified some level of parallelism in the build, then this can quickly use up your available RAM.

Try installing using only 1 core at a time:

Sys.setenv(MAKEFLAGS = "-1")
install.packages("rstanarm", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

I follow your instructions to run those 2 lines of code and get below:

/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/src/stan/mcmc/hmc/hamiltonians/dense_e_metric.hpp:23:56: required from ‘double stan::mcmc::dense_e_metric<Model, BaseRNG>::T(stan::mcmc::dense_e_point&) [with Model = model_bernoulli_namespace::model_bernoulli; BaseRNG = boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399> >]’
stan_files/bernoulli.cc:29:1: required from here
/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/DenseCoeffsBase.h:55:30: warning: ignoring attributes on template argument ‘Eigen::internal::packet_traits::type {aka __vector(2) double}’ [-Wignored-attributes]
g++: internal compiler error: Killed (program cc1plus)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions.
make: *** [stan_files/bernoulli.o] Error 4
ERROR: compilation failed for package ‘rstanarm’

  • removing ‘/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/rstanarm’
    /usr/lib/R/etc/Makeconf:175: recipe for target ‘stan_files/bernoulli.o’ failed
    rm stan_files/jm.cc stan_files/bernoulli.cc stan_files/binomial.cc stan_files/lm.cc stan_files/count.cc stan_files/mvmer.cc

The downloaded source packages are in
‘/tmp/RtmpW9G5p0/downloaded_packages’

How much memory/RAM does your computer have?

Are you able to run the RStan example model:

example(stan_model,package="rstan",run.dontrun=T)
> example(stan_model,package="rstan",run.dontrun=T)

stn_md> stancode <- 'data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}'

stn_md> mod <- stan_model(model_code = stancode, verbose = TRUE)

TRANSLATING MODEL '16a540c6086086816528e4524def24d9' FROM Stan CODE TO C++ CODE NOW.
successful in parsing the Stan model '16a540c6086086816528e4524def24d9'.
COMPILING THE C++ CODE FOR MODEL '16a540c6086086816528e4524def24d9' NOW.
OS: x86_64, linux-gnu; rstan: 2.21.2; Rcpp: 1.0.7; inline: 0.3.19 
 >> setting environment variables: 
PKG_LIBS =  '/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/rstan/lib//libStanServices.a' -L'/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/lib/' -lStanHeaders -L'/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppParallel/lib/' -ltbb
PKG_CPPFLAGS =   -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/Rcpp/include/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/unsupported"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/BH/include" -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/src/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppParallel/include/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 
 >> Program source :

   1 : 
   2 : // includes from the plugin
   3 : // [[Rcpp::plugins(cpp14)]]
   4 : 
   5 : 
   6 : // user includes
   7 : #include <Rcpp.h>
   8 : #include <rstan/io/rlist_ref_var_context.hpp>
   9 : #include <rstan/io/r_ostream.hpp>
  10 : #include <rstan/stan_args.hpp>
  11 : #include <boost/integer/integer_log2.hpp>
  12 : // Code generated by Stan version 2.21.0
  13 : 
  14 : #include <stan/model/model_header.hpp>
  15 : 
  16 : namespace model1c9126394050_16a540c6086086816528e4524def24d9_namespace {
  17 : 
  18 : using std::istream;
  19 : using std::string;
  20 : using std::stringstream;
  21 : using std::vector;
  22 : using stan::io::dump;
  23 : using stan::math::lgamma;
  24 : using stan::model::prob_grad;
  25 : using namespace stan::math;
  26 : 
  27 : static int current_statement_begin__;
  28 : 
  29 : stan::io::program_reader prog_reader__() {
  30 :     stan::io::program_reader reader;
  31 :     reader.add_event(0, 0, "start", "model1c9126394050_16a540c6086086816528e4524def24d9");
  32 :     reader.add_event(3, 1, "end", "model1c9126394050_16a540c6086086816528e4524def24d9");
  33 :     return reader;
  34 : }
  35 : 
  36 : class model1c9126394050_16a540c6086086816528e4524def24d9
  37 :   : public stan::model::model_base_crtp<model1c9126394050_16a540c6086086816528e4524def24d9> {
  38 : private:
  39 :         double y_mean;
  40 : public:
  41 :     model1c9126394050_16a540c6086086816528e4524def24d9(rstan::io::rlist_ref_var_context& context__,
  42 :         std::ostream* pstream__ = 0)
  43 :         : model_base_crtp(0) {
  44 :         ctor_body(context__, 0, pstream__);
  45 :     }
  46 : 
  47 :     model1c9126394050_16a540c6086086816528e4524def24d9(stan::io::var_context& context__,
  48 :         unsigned int random_seed__,
  49 :         std::ostream* pstream__ = 0)
  50 :         : model_base_crtp(0) {
  51 :         ctor_body(context__, random_seed__, pstream__);
  52 :     }
  53 : 
  54 :     void ctor_body(stan::io::var_context& context__,
  55 :                    unsigned int random_seed__,
  56 :                    std::ostream* pstream__) {
  57 :         typedef double local_scalar_t__;
  58 : 
  59 :         boost::ecuyer1988 base_rng__ =
  60 :           stan::services::util::create_rng(random_seed__, 0);
  61 :         (void) base_rng__;  // suppress unused var warning
  62 : 
  63 :         current_statement_begin__ = -1;
  64 : 
  65 :         static const char* function__ = "model1c9126394050_16a540c6086086816528e4524def24d9_namespace::model1c9126394050_16a540c6086086816528e4524def24d9";
  66 :         (void) function__;  // dummy to suppress unused var warning
  67 :         size_t pos__;
  68 :         (void) pos__;  // dummy to suppress unused var warning
  69 :         std::vector<int> vals_i__;
  70 :         std::vector<double> vals_r__;
  71 :         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
  72 :         (void) DUMMY_VAR__;  // suppress unused var warning
  73 : 
  74 :         try {
  75 :             // initialize data block variables from context__
  76 :             current_statement_begin__ = 1;
  77 :             context__.validate_dims("data initialization", "y_mean", "double", context__.to_vec());
  78 :             y_mean = double(0);
  79 :             vals_r__ = context__.vals_r("y_mean");
  80 :             pos__ = 0;
  81 :             y_mean = vals_r__[pos__++];
  82 : 
  83 : 
  84 :             // initialize transformed data variables
  85 :             // execute transformed data statements
  86 : 
  87 :             // validate transformed data
  88 : 
  89 :             // validate, set parameter ranges
  90 :             num_params_r__ = 0U;
  91 :             param_ranges_i__.clear();
  92 :             current_statement_begin__ = 1;
  93 :             num_params_r__ += 1;
  94 :         } catch (const std::exception& e) {
  95 :             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
  96 :             // Next line prevents compiler griping about no return
  97 :             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
  98 :         }
  99 :     }
 100 : 
 101 :     ~model1c9126394050_16a540c6086086816528e4524def24d9() { }
 102 : 
 103 : 
 104 :     void transform_inits(const stan::io::var_context& context__,
 105 :                          std::vector<int>& params_i__,
 106 :                          std::vector<double>& params_r__,
 107 :                          std::ostream* pstream__) const {
 108 :         typedef double local_scalar_t__;
 109 :         stan::io::writer<double> writer__(params_r__, params_i__);
 110 :         size_t pos__;
 111 :         (void) pos__; // dummy call to supress warning
 112 :         std::vector<double> vals_r__;
 113 :         std::vector<int> vals_i__;
 114 : 
 115 :         current_statement_begin__ = 1;
 116 :         if (!(context__.contains_r("y")))
 117 :             stan::lang::rethrow_located(std::runtime_error(std::string("Variable y missing")), current_statement_begin__, prog_reader__());
 118 :         vals_r__ = context__.vals_r("y");
 119 :         pos__ = 0U;
 120 :         context__.validate_dims("parameter initialization", "y", "double", context__.to_vec());
 121 :         double y(0);
 122 :         y = vals_r__[pos__++];
 123 :         try {
 124 :             writer__.scalar_unconstrain(y);
 125 :         } catch (const std::exception& e) {
 126 :             stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable y: ") + e.what()), current_statement_begin__, prog_reader__());
 127 :         }
 128 : 
 129 :         params_r__ = writer__.data_r();
 130 :         params_i__ = writer__.data_i();
 131 :     }
 132 : 
 133 :     void transform_inits(const stan::io::var_context& context,
 134 :                          Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
 135 :                          std::ostream* pstream__) const {
 136 :       std::vector<double> params_r_vec;
 137 :       std::vector<int> params_i_vec;
 138 :       transform_inits(context, params_i_vec, params_r_vec, pstream__);
 139 :       params_r.resize(params_r_vec.size());
 140 :       for (int i = 0; i < params_r.size(); ++i)
 141 :         params_r(i) = params_r_vec[i];
 142 :     }
 143 : 
 144 : 
 145 :     template <bool propto__, bool jacobian__, typename T__>
 146 :     T__ log_prob(std::vector<T__>& params_r__,
 147 :                  std::vector<int>& params_i__,
 148 :                  std::ostream* pstream__ = 0) const {
 149 : 
 150 :         typedef T__ local_scalar_t__;
 151 : 
 152 :         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
 153 :         (void) DUMMY_VAR__;  // dummy to suppress unused var warning
 154 : 
 155 :         T__ lp__(0.0);
 156 :         stan::math::accumulator<T__> lp_accum__;
 157 :         try {
 158 :             stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
 159 : 
 160 :             // model parameters
 161 :             current_statement_begin__ = 1;
 162 :             local_scalar_t__ y;
 163 :             (void) y;  // dummy to suppress unused var warning
 164 :             if (jacobian__)
 165 :                 y = in__.scalar_constrain(lp__);
 166 :             else
 167 :                 y = in__.scalar_constrain();
 168 : 
 169 :             // model body
 170 : 
 171 :             current_statement_begin__ = 1;
 172 :             lp_accum__.add(normal_log<propto__>(y, y_mean, 1));
 173 : 
 174 :         } catch (const std::exception& e) {
 175 :             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
 176 :             // Next line prevents compiler griping about no return
 177 :             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
 178 :         }
 179 : 
 180 :         lp_accum__.add(lp__);
 181 :         return lp_accum__.sum();
 182 : 
 183 :     } // log_prob()
 184 : 
 185 :     template <bool propto, bool jacobian, typename T_>
 186 :     T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
 187 :                std::ostream* pstream = 0) const {
 188 :       std::vector<T_> vec_params_r;
 189 :       vec_params_r.reserve(params_r.size());
 190 :       for (int i = 0; i < params_r.size(); ++i)
 191 :         vec_params_r.push_back(params_r(i));
 192 :       std::vector<int> vec_params_i;
 193 :       return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
 194 :     }
 195 : 
 196 : 
 197 :     void get_param_names(std::vector<std::string>& names__) const {
 198 :         names__.resize(0);
 199 :         names__.push_back("y");
 200 :     }
 201 : 
 202 : 
 203 :     void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
 204 :         dimss__.resize(0);
 205 :         std::vector<size_t> dims__;
 206 :         dims__.resize(0);
 207 :         dimss__.push_back(dims__);
 208 :     }
 209 : 
 210 :     template <typename RNG>
 211 :     void write_array(RNG& base_rng__,
 212 :                      std::vector<double>& params_r__,
 213 :                      std::vector<int>& params_i__,
 214 :                      std::vector<double>& vars__,
 215 :                      bool include_tparams__ = true,
 216 :                      bool include_gqs__ = true,
 217 :                      std::ostream* pstream__ = 0) const {
 218 :         typedef double local_scalar_t__;
 219 : 
 220 :         vars__.resize(0);
 221 :         stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
 222 :         static const char* function__ = "model1c9126394050_16a540c6086086816528e4524def24d9_namespace::write_array";
 223 :         (void) function__;  // dummy to suppress unused var warning
 224 : 
 225 :         // read-transform, write parameters
 226 :         double y = in__.scalar_constrain();
 227 :         vars__.push_back(y);
 228 : 
 229 :         double lp__ = 0.0;
 230 :         (void) lp__;  // dummy to suppress unused var warning
 231 :         stan::math::accumulator<double> lp_accum__;
 232 : 
 233 :         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
 234 :         (void) DUMMY_VAR__;  // suppress unused var warning
 235 : 
 236 :         if (!include_tparams__ && !include_gqs__) return;
 237 : 
 238 :         try {
 239 :             if (!include_gqs__ && !include_tparams__) return;
 240 :             if (!include_gqs__) return;
 241 :         } catch (const std::exception& e) {
 242 :             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
 243 :             // Next line prevents compiler griping about no return
 244 :             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
 245 :         }
 246 :     }
 247 : 
 248 :     template <typename RNG>
 249 :     void write_array(RNG& base_rng,
 250 :                      Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
 251 :                      Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
 252 :                      bool include_tparams = true,
 253 :                      bool include_gqs = true,
 254 :                      std::ostream* pstream = 0) const {
 255 :       std::vector<double> params_r_vec(params_r.size());
 256 :       for (int i = 0; i < params_r.size(); ++i)
 257 :         params_r_vec[i] = params_r(i);
 258 :       std::vector<double> vars_vec;
 259 :       std::vector<int> params_i_vec;
 260 :       write_array(base_rng, params_r_vec, params_i_vec, vars_vec, include_tparams, include_gqs, pstream);
 261 :       vars.resize(vars_vec.size());
 262 :       for (int i = 0; i < vars.size(); ++i)
 263 :         vars(i) = vars_vec[i];
 264 :     }
 265 : 
 266 :     std::string model_name() const {
 267 :         return "model1c9126394050_16a540c6086086816528e4524def24d9";
 268 :     }
 269 : 
 270 : 
 271 :     void constrained_param_names(std::vector<std::string>& param_names__,
 272 :                                  bool include_tparams__ = true,
 273 :                                  bool include_gqs__ = true) const {
 274 :         std::stringstream param_name_stream__;
 275 :         param_name_stream__.str(std::string());
 276 :         param_name_stream__ << "y";
 277 :         param_names__.push_back(param_name_stream__.str());
 278 : 
 279 :         if (!include_gqs__ && !include_tparams__) return;
 280 : 
 281 :         if (include_tparams__) {
 282 :         }
 283 : 
 284 :         if (!include_gqs__) return;
 285 :     }
 286 : 
 287 : 
 288 :     void unconstrained_param_names(std::vector<std::string>& param_names__,
 289 :                                    bool include_tparams__ = true,
 290 :                                    bool include_gqs__ = true) const {
 291 :         std::stringstream param_name_stream__;
 292 :         param_name_stream__.str(std::string());
 293 :         param_name_stream__ << "y";
 294 :         param_names__.push_back(param_name_stream__.str());
 295 : 
 296 :         if (!include_gqs__ && !include_tparams__) return;
 297 : 
 298 :         if (include_tparams__) {
 299 :         }
 300 : 
 301 :         if (!include_gqs__) return;
 302 :     }
 303 : 
 304 : }; // model
 305 : 
 306 : }  // namespace
 307 : 
 308 : typedef model1c9126394050_16a540c6086086816528e4524def24d9_namespace::model1c9126394050_16a540c6086086816528e4524def24d9 stan_model;
 309 : 
 310 : #ifndef USING_R
 311 : 
 312 : stan::model::model_base& new_model(
 313 :         stan::io::var_context& data_context,
 314 :         unsigned int seed,
 315 :         std::ostream* msg_stream) {
 316 :   stan_model* m = new stan_model(data_context, seed, msg_stream);
 317 :   return *m;
 318 : }
 319 : 
 320 : #endif
 321 : 
 322 : 
 323 : 
 324 : #include <rstan_next/stan_fit.hpp>
 325 : 
 326 : struct stan_model_holder {
 327 :     stan_model_holder(rstan::io::rlist_ref_var_context rcontext,
 328 :                       unsigned int random_seed)
 329 :     : rcontext_(rcontext), random_seed_(random_seed)
 330 :      {
 331 :      }
 332 : 
 333 :    //stan::math::ChainableStack ad_stack;
 334 :    rstan::io::rlist_ref_var_context rcontext_;
 335 :    unsigned int random_seed_;
 336 : };
 337 : 
 338 : Rcpp::XPtr<stan::model::model_base> model_ptr(stan_model_holder* smh) {
 339 :   Rcpp::XPtr<stan::model::model_base> model_instance(new stan_model(smh->rcontext_, smh->random_seed_), true);
 340 :   return model_instance;
 341 : }
 342 : 
 343 : Rcpp::XPtr<rstan::stan_fit_base> fit_ptr(stan_model_holder* smh) {
 344 :   return Rcpp::XPtr<rstan::stan_fit_base>(new rstan::stan_fit(model_ptr(smh), smh->random_seed_), true);
 345 : }
 346 : 
 347 : std::string model_name(stan_model_holder* smh) {
 348 :   return model_ptr(smh).get()->model_name();
 349 : }
 350 : 
 351 : RCPP_MODULE(stan_fit4model1c9126394050_16a540c6086086816528e4524def24d9_mod){
 352 :   Rcpp::class_<stan_model_holder>("stan_fit4model1c9126394050_16a540c6086086816528e4524def24d9")
 353 :   .constructor<rstan::io::rlist_ref_var_context, unsigned int>()
 354 :   .method("model_ptr", &model_ptr)
 355 :   .method("fit_ptr", &fit_ptr)
 356 :   .method("model_name", &model_name)
 357 :   ;
 358 : }
 359 : 
 360 : 
 361 : // declarations
 362 : extern "C" {
 363 : SEXP file1c91487e980e( ) ;
 364 : }
 365 : 
 366 : // definition
 367 : SEXP file1c91487e980e() {
 368 :  return Rcpp::wrap("16a540c6086086816528e4524def24d9");
 369 : }
make cmd is
  make -f '/usr/lib/R/etc/Makeconf' -f '/usr/share/R/share/make/[shlib.mk](http://shlib.mk)' CXX='$(CXX14) $(CXX14STD)' CXXFLAGS='$(CXX14FLAGS)' CXXPICFLAGS='$(CXX14PICFLAGS)' SHLIB_LDFLAGS='$(SHLIB_CXX14LDFLAGS)' SHLIB_LD='$(SHLIB_CXX14LD)' SHLIB='file1c91487e980e.so' OBJECTS='file1c91487e980e.o'

make would use
g++ -std=gnu++14 -I"/usr/share/R/include" -DNDEBUG   -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/Rcpp/include/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/unsupported"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/BH/include" -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/src/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppParallel/include/"  -I"/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1      -fpic  -g -O2 -fdebug-prefix-map=/build/r-base-vB4ZXq/r-base-4.1.0=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -g  -c file1c91487e980e.cpp -o file1c91487e980e.o
if test  "zfile1c91487e980e.o" != "z"; then \
  echo g++ -std=gnu++14 -shared -L"/usr/lib/R/lib" -Wl,-Bsymbolic-functions -Wl,-z,relro -o file1c91487e980e.so file1c91487e980e.o  '/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/rstan/lib//libStanServices.a' -L'/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/lib/' -lStanHeaders -L'/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppParallel/lib/' -ltbb  -L"/usr/lib/R/lib" -lR; \
  g++ -std=gnu++14 -shared -L"/usr/lib/R/lib" -Wl,-Bsymbolic-functions -Wl,-z,relro -o file1c91487e980e.so file1c91487e980e.o  '/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/rstan/lib//libStanServices.a' -L'/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/StanHeaders/lib/' -lStanHeaders -L'/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppParallel/lib/' -ltbb  -L"/usr/lib/R/lib" -lR; \
fi

stn_md> fit <- sampling(mod, data = list(y_mean = 0))

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 1.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
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Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 0.006707 seconds (Warm-up)
Chain 1:                0.006945 seconds (Sampling)
Chain 1:                0.013652 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 7e-06 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
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Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 2: 
Chain 2:  Elapsed Time: 0.007061 seconds (Warm-up)
Chain 2:                0.006988 seconds (Sampling)
Chain 2:                0.014049 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 6e-06 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
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Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 3: 
Chain 3:  Elapsed Time: 0.006922 seconds (Warm-up)
Chain 3:                0.006071 seconds (Sampling)
Chain 3:                0.012993 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 5e-06 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
Chain 4: 
Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
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Chain 4: 
Chain 4:  Elapsed Time: 0.00682 seconds (Warm-up)
Chain 4:                0.006731 seconds (Sampling)
Chain 4:                0.013551 seconds (Total)
Chain 4: 

stn_md> fit2 <- sampling(mod, data = list(y_mean = 5))

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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Chain 1: 
Chain 1:  Elapsed Time: 0.0067 seconds (Warm-up)
Chain 1:                0.006808 seconds (Sampling)
Chain 1:                0.013508 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 6e-06 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
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Chain 2: 
Chain 2:  Elapsed Time: 0.006901 seconds (Warm-up)
Chain 2:                0.006555 seconds (Sampling)
Chain 2:                0.013456 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 5e-06 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
Chain 3: 
Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
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Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 3: 
Chain 3:  Elapsed Time: 0.007171 seconds (Warm-up)
Chain 3:                0.006281 seconds (Sampling)
Chain 3:                0.013452 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 5e-06 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
Chain 4: 
Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
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Chain 4: 
Chain 4:  Elapsed Time: 0.006721 seconds (Warm-up)
Chain 4:                0.006178 seconds (Sampling)
Chain 4:                0.012899 seconds (Total)
Chain 4: 

I am running RStudio on Azure VM, of Size Standard B2s with with 2GB Ram as shown.

Ah that’s likely the issue. The rstanarm models will need >3gb RAM to compile and install

let me increase the ram size and let you know. I remember I did that, and I go the same error issue.

my present RAM is 4GB, which is more than 3gb RAM, isn’t it?
Let me increase to 8GB and show you that I get the same error.

now RAM is 16GB RAM, and I would restart my RStudio.

And you get the same error? What command are you using to install rstanarm?

stan_files/continuous.cc:29:1: required from here
/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/CoreEvaluators.h:960:8: warning: ignoring attributes on template argument ‘Eigen::internal::packet_traits::type {aka __vector(2) double}’ [-Wignored-attributes]
g++: internal compiler error: Killed (program cc1plus)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions.
make: *** [stan_files/continuous.o] Error 4
/usr/lib/R/etc/Makeconf:175: recipe for target ‘stan_files/continuous.o’ failed
rm stan_files/jm.cc stan_files/continuous.cc stan_files/bernoulli.cc stan_files/binomial.cc stan_files/lm.cc stan_files/polr.cc stan_files/count.cc stan_files/mvmer.cc
ERROR: compilation failed for package ‘rstanarm’

  • removing ‘/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1/rstanarm’
    Warning in install.packages :
    installation of package ‘rstanarm’ had non-zero exit status

The downloaded source packages are in
‘/tmp/RtmpXWRyQ4/downloaded_packages’

The same error even after increase to 16GB RAM

Restarting R session…

Sys.setenv(MAKEFLAGS = “-1”)
install.packages(“rstanarm”, repos = c(“Repository for distributing (some) stan-dev R packages | r-packages”, getOption(“repos”)))
Installing package into ‘/home/Hutchinson/R/x86_64-pc-linux-gnu-library/4.1’
(as ‘lib’ is unspecified)
trying URL ‘https://mc-stan.org/r-packages/src/contrib/rstanarm_2.21.2.tar.gz
Content type ‘application/gzip’ length 755170 bytes (737 KB)
==================================================
downloaded 737 KB

  • installing source package ‘rstanarm’ …
    ** using staged installation
    ** libs

Ah sorry, I’d mistyped in my earlier post. That should be (note the j):

Sys.setenv(MAKEFLAGS="-j1")