bug#50841: rstan needs gfortran 9+

bug#50841: rstan needs gfortran 9+

[Ricardo Wurmus]

Hi,

thank you for your bug report!

> You find a related error if you try install.packages("isoband"), which could work as a test case:
>
> /gnu/store/741057r2x06zwg6zcmqmdyv51spm6n9i-gfortran-7.5.0-lib/lib/libstdc++.so.6: version
> `GLIBCXX_3.4.26' not found (required by
> /home/kept/Dotfiles/R/x86_64-unknown-linux-gnu-library/4.1/00LOCK-isoband/00new/isoband/libs/isoband.so)

“install.packages” will only work if you carefully control your
environment.  It will not do the right thing when you use it on a
foreign distro, for example, because it would likely pick the system’s
compiler toolchain to build things that are eventually incompatible with
the libraries used by R from Guix.

Here’s an old blog post that explains the problem and how to work around
it if you must use “install.packages”:

    https://elephly.net/posts/2017-03-24-r-with-guix.html

I haven’t been able to reproduce your problem, though.  Here’s what I did:

--8<---------------cut here---------------start------------->8---
$ guix shell -C r-minimal r-rstan -D r-minimal -- R

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> library(rstan)
Loading required package: StanHeaders
Loading required package: ggplot2
code for methods in class "Rcpp_model_base" was not checked for suspicious field assignments (recommended package 'codetools' not available?)
code for methods in class "Rcpp_model_base" was not checked for suspicious field assignments (recommended package 'codetools' not available?)
code for methods in class "Rcpp_stan_fit" was not checked for suspicious field assignments (recommended package 'codetools' not available?)
code for methods in class "Rcpp_stan_fit" was not checked for suspicious field assignments (recommended package 'codetools' not available?)
rstan (Version 2.21.5, GitRev: 2e1f913d3ca3)
For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
>      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) 
+ + + + + + + + > 
fit1
code for methods in class "Rcpp_stan_fit4model139796fca_b524cd829fcb9f50f6761f2451b62eec" was not checked for suspicious field assignments (recommended package 'codetools' not available?)
code for methods in class "Rcpp_stan_fit4model139796fca_b524cd829fcb9f50f6761f2451b62eec" was not checked for suspicious field assignments (recommended package 'codetools' not available?)

SAMPLING FOR MODEL 'b524cd829fcb9f50f6761f2451b62eec' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 6e-06 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: WARNING: No variance estimation is
Chain 1:          performed for num_warmup < 20
Chain 1: 
Chain 1: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%]  (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%]  (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%]  (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%]  (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%]  (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%]  (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%]  (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%]  (Sampling)
Chain 1: Iteration: 10 / 10 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 0.000157 seconds (Warm-up)
Chain 1:                0.000123 seconds (Sampling)
Chain 1:                0.00028 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'b524cd829fcb9f50f6761f2451b62eec' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 3e-06 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
Chain 2: 
Chain 2: WARNING: No variance estimation is
Chain 2:          performed for num_warmup < 20
Chain 2: 
Chain 2: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 2: Iteration: 2 / 10 [ 20%]  (Warmup)
Chain 2: Iteration: 3 / 10 [ 30%]  (Warmup)
Chain 2: Iteration: 4 / 10 [ 40%]  (Warmup)
Chain 2: Iteration: 5 / 10 [ 50%]  (Warmup)
Chain 2: Iteration: 6 / 10 [ 60%]  (Sampling)
Chain 2: Iteration: 7 / 10 [ 70%]  (Sampling)
Chain 2: Iteration: 8 / 10 [ 80%]  (Sampling)
Chain 2: Iteration: 9 / 10 [ 90%]  (Sampling)
Chain 2: Iteration: 10 / 10 [100%]  (Sampling)
Chain 2: 
Chain 2:  Elapsed Time: 0.000146 seconds (Warm-up)
Chain 2:                0.000132 seconds (Sampling)
Chain 2:                0.000278 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL 'b524cd829fcb9f50f6761f2451b62eec' 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: 
Chain 3: 
Chain 3: WARNING: No variance estimation is
Chain 3:          performed for num_warmup < 20
Chain 3: 
Chain 3: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 3: Iteration: 2 / 10 [ 20%]  (Warmup)
Chain 3: Iteration: 3 / 10 [ 30%]  (Warmup)
Chain 3: Iteration: 4 / 10 [ 40%]  (Warmup)
Chain 3: Iteration: 5 / 10 [ 50%]  (Warmup)
Chain 3: Iteration: 6 / 10 [ 60%]  (Sampling)
Chain 3: Iteration: 7 / 10 [ 70%]  (Sampling)
Chain 3: Iteration: 8 / 10 [ 80%]  (Sampling)
Chain 3: Iteration: 9 / 10 [ 90%]  (Sampling)
Chain 3: Iteration: 10 / 10 [100%]  (Sampling)
Chain 3: 
Chain 3:  Elapsed Time: 0.000271 seconds (Warm-up)
Chain 3:                0.000253 seconds (Sampling)
Chain 3:                0.000524 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL 'b524cd829fcb9f50f6761f2451b62eec' 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: WARNING: No variance estimation is
Chain 4:          performed for num_warmup < 20
Chain 4: 
Chain 4: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 4: Iteration: 2 / 10 [ 20%]  (Warmup)
Chain 4: Iteration: 3 / 10 [ 30%]  (Warmup)
Chain 4: Iteration: 4 / 10 [ 40%]  (Warmup)
Chain 4: Iteration: 5 / 10 [ 50%]  (Warmup)
Chain 4: Iteration: 6 / 10 [ 60%]  (Sampling)
Chain 4: Iteration: 7 / 10 [ 70%]  (Sampling)
Chain 4: Iteration: 8 / 10 [ 80%]  (Sampling)
Chain 4: Iteration: 9 / 10 [ 90%]  (Sampling)
Chain 4: Iteration: 10 / 10 [100%]  (Sampling)
Chain 4: 
Chain 4:  Elapsed Time: 0.00014 seconds (Warm-up)
Chain 4:                0.000132 seconds (Sampling)
Chain 4:                0.000272 seconds (Total)
Chain 4: 
Warning message:
The largest R-hat is 1.07, indicating chains have not mixed.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#r-hat 
> > Inference for Stan model: b524cd829fcb9f50f6761f2451b62eec.
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] -0.02    0.20 1.02 -1.80 -0.68 -0.05  0.59  1.86    26 0.89
y[2]  2.06    0.49 1.81 -1.29  0.78  1.96  3.48  5.00    14 1.08
lp__ -1.65    0.17 0.85 -3.08 -2.20 -1.64 -1.01 -0.41    26 0.81

Samples were drawn using NUTS(diag_e) at Tue Jul 12 17:28:00 2022.
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).
> 
--8<---------------cut here---------------end--------------->8---

Could you please show us an example like the above that fails when run
in “guix shell -C”?  Please also provide the output of “guix describe -f
channels”.
Thank you!

-- 
Ricardo