* With RStan

#+name: normal-stan
#+begin_src stan :file model.stan
  data {
    int<lower=1> N;
    vector[N] x;
  }

  parameters {
    real mu;
    real<lower=0> std;
  }

  model {
    x ~ normal(mu, std);
  }
#+end_src

#+RESULTS: normal-stan
[[file:model.stan]]

#+begin_src R :session *R* :var model=normal-stan :results silent
  library(rstan)

  N <- 50
  x <- rnorm(N, 20, 3)

  fit <- stan(file=model, data=list(N=N, x=x))
#+end_src

* With CmdStan

#+begin_src elisp :results silent
  (setq org-babel-stan-cmdstan-directory "~/src/cmdstan/")
#+end_src

#+name: normal-compile
#+begin_src stan :file normal
  data {
    int<lower=1> N;
    vector[N] x;
  }

  parameters {
    real mu;
    real<lower=0> std;
  }

  model {
    x ~ normal(mu, std);
  }
#+end_src

#+RESULTS: normal-compile
[[file:normal]]

#+begin_src R :session *R* :results silent
  stan_rdump(c('N', 'x'), 'normal.data.R')
#+end_src

#+begin_src sh :results output drawer
  ./normal sample data file=normal.data.R
#+end_src

#+RESULTS:
:RESULTS:
 method = sample (Default)
   sample
     num_samples = 1000 (Default)
     num_warmup = 1000 (Default)
     save_warmup = 0 (Default)
     thin = 1 (Default)
     adapt
       engaged = 1 (Default)
       gamma = 0.050000000000000003 (Default)
       delta = 0.80000000000000004 (Default)
       kappa = 0.75 (Default)
       t0 = 10 (Default)
       init_buffer = 75 (Default)
       term_buffer = 50 (Default)
       window = 25 (Default)
     algorithm = hmc (Default)
       hmc
         engine = nuts (Default)
           nuts
             max_depth = 10 (Default)
         metric = diag_e (Default)
         stepsize = 1 (Default)
         stepsize_jitter = 0 (Default)
 id = 0 (Default)
 data
   file = normal.data.R
 init = 2 (Default)
 random
   seed = 1573443700
 output
   file = output.csv (Default)
   diagnostic_file =  (Default)
   refresh = 100 (Default)


Gradient evaluation took 4e-06 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
Adjust your expectations accordingly!


Iteration:    1 / 2000 [  0%]  (Warmup)

Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
stan::prob::normal_log: Scale parameter is 0, but must be > 0!
If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
Iteration:  100 / 2000 [  5%]  (Warmup)
Iteration:  200 / 2000 [ 10%]  (Warmup)
Iteration:  300 / 2000 [ 15%]  (Warmup)
Iteration:  400 / 2000 [ 20%]  (Warmup)
Iteration:  500 / 2000 [ 25%]  (Warmup)
Iteration:  600 / 2000 [ 30%]  (Warmup)
Iteration:  700 / 2000 [ 35%]  (Warmup)
Iteration:  800 / 2000 [ 40%]  (Warmup)
Iteration:  900 / 2000 [ 45%]  (Warmup)
Iteration: 1000 / 2000 [ 50%]  (Warmup)
Iteration: 1001 / 2000 [ 50%]  (Sampling)
Iteration: 1100 / 2000 [ 55%]  (Sampling)
Iteration: 1200 / 2000 [ 60%]  (Sampling)
Iteration: 1300 / 2000 [ 65%]  (Sampling)
Iteration: 1400 / 2000 [ 70%]  (Sampling)
Iteration: 1500 / 2000 [ 75%]  (Sampling)
Iteration: 1600 / 2000 [ 80%]  (Sampling)
Iteration: 1700 / 2000 [ 85%]  (Sampling)
Iteration: 1800 / 2000 [ 90%]  (Sampling)
Iteration: 1900 / 2000 [ 95%]  (Sampling)
Iteration: 2000 / 2000 [100%]  (Sampling)

#  Elapsed Time: 0.013356 seconds (Warm-up)
#                0.024708 seconds (Sampling)
#                0.038064 seconds (Total)

:END: