Getting started with CmdStanR

Jonah Gabry, Rok Češnovar, and Andrew Johnson

Source: vignettes/cmdstanr.Rmd

Introduction

CmdStanR (Command Stan R) is a lightweight interface to Stan for R users that provides an alternative to the traditional RStan interface. See the Comparison with RStan section later in this vignette for more details on how the two interfaces differ.

Using CmdStanR requires installing the cmdstanr R package and also CmdStan, the command line interface to Stan. First we install the R package by running the following command in R.

# we recommend running this is a fresh R session or restarting your current session
install.packages("cmdstanr", repos = c('https://stan-dev.r-universe.dev', getOption("repos")))

We can now load the package like any other R package. We’ll also load the bayesplot and posterior packages to use later in examples.

Installing CmdStan

CmdStanR requires a working installation of CmdStan, the shell interface to Stan. If you don’t have CmdStan installed then CmdStanR can install it for you, assuming you have a suitable C++ toolchain. The requirements are described in the CmdStan Guide:

To double check that your toolchain is set up properly you can call the check_cmdstan_toolchain() function:

The C++ toolchain required for CmdStan is setup properly!

If your toolchain is configured correctly then CmdStan can be installed by calling the install_cmdstan() function:

install_cmdstan(cores = 2)

Before CmdStanR can be used it needs to know where the CmdStan installation is located. When the package is loaded it tries to help automate this to avoid having to manually set the path every session:

  1. If the environment variable "CMDSTAN" exists at load time then its value will be automatically set as the default path to CmdStan for the R session. This is useful if your CmdStan installation is not located in the default directory that would have been used by install_cmdstan() (see #2).

  2. If no environment variable is found when loaded but any directory in the form ".cmdstan/cmdstan-[version]", for example ".cmdstan/cmdstan-2.23.0", exists in the user’s home directory (Sys.getenv("HOME"), not the current working directory) then the path to the CmdStan with the largest version number will be set as the path to CmdStan for the R session. This is the same as the default directory that install_cmdstan() uses to install the latest version of CmdStan, so if that’s how you installed CmdStan you shouldn’t need to manually set the path to CmdStan when loading CmdStanR.

If neither of these applies (or you want to subsequently change the path) you can use the set_cmdstan_path() function:

set_cmdstan_path(PATH_TO_CMDSTAN)

To check the path to the CmdStan installation and the CmdStan version number you can use cmdstan_path() and cmdstan_version():

[1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0"
[1] "2.36.0"

Compiling a model

The cmdstan_model() function creates a new CmdStanModel object from a file containing a Stan program. Under the hood, CmdStan is called to translate a Stan program to C++ and create a compiled executable. Here we’ll use the example Stan program that comes with the CmdStan installation:

file <- file.path(cmdstan_path(), "examples", "bernoulli", "bernoulli.stan")
mod <- cmdstan_model(file)

The object mod is an R6 reference object of class CmdStanModel and behaves similarly to R’s reference class objects and those in object oriented programming languages. Methods are accessed using the $ operator. This design choice allows for CmdStanR and CmdStanPy to provide a similar user experience and share many implementation details.

The Stan program can be printed using the $print() method:

mod$print()
data {
  int<lower=0> N;
  array[N] int<lower=0, upper=1> y;
}
parameters {
  real<lower=0, upper=1> theta;
}
model {
  theta ~ beta(1, 1); // uniform prior on interval 0,1
  y ~ bernoulli(theta);
}

The path to the compiled executable is returned by the $exe_file() method:

mod$exe_file()
[1] "/Users/jgabry/.cmdstan/cmdstan-2.36.0/examples/bernoulli/bernoulli"

Running MCMC

The $sample() method for CmdStanModel objects runs Stan’s default MCMC algorithm. The data argument accepts a named list of R objects (like for RStan) or a path to a data file compatible with CmdStan (JSON or R dump).

# names correspond to the data block in the Stan program
data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))

fit <- mod$sample(
  data = data_list,
  seed = 123,
  chains = 4,
  parallel_chains = 4,
  refresh = 500 # print update every 500 iters
)
Running MCMC with 4 parallel chains...

Chain 1 Iteration:    1 / 2000 [  0%]  (Warmup) 
Chain 1 Iteration:  500 / 2000 [ 25%]  (Warmup) 
Chain 1 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
Chain 1 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
Chain 1 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
Chain 1 Iteration: 2000 / 2000 [100%]  (Sampling) 
Chain 2 Iteration:    1 / 2000 [  0%]  (Warmup) 
Chain 2 Iteration:  500 / 2000 [ 25%]  (Warmup) 
Chain 2 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
Chain 2 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
Chain 2 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
Chain 2 Iteration: 2000 / 2000 [100%]  (Sampling) 
Chain 3 Iteration:    1 / 2000 [  0%]  (Warmup) 
Chain 3 Iteration:  500 / 2000 [ 25%]  (Warmup) 
Chain 3 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
Chain 3 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
Chain 3 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
Chain 3 Iteration: 2000 / 2000 [100%]  (Sampling) 
Chain 4 Iteration:    1 / 2000 [  0%]  (Warmup) 
Chain 4 Iteration:  500 / 2000 [ 25%]  (Warmup) 
Chain 4 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
Chain 4 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
Chain 4 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
Chain 4 Iteration: 2000 / 2000 [100%]  (Sampling) 
Chain 1 finished in 0.0 seconds.
Chain 2 finished in 0.0 seconds.
Chain 3 finished in 0.0 seconds.
Chain 4 finished in 0.0 seconds.

All 4 chains finished successfully.
Mean chain execution time: 0.0 seconds.
Total execution time: 0.4 seconds.

There are many more arguments that can be passed to the $sample() method. For details follow this link to its separate documentation page:

The $sample() method creates R6 CmdStanMCMC objects, which have many associated methods. Below we will demonstrate some of the most important methods. For a full list, follow this link to the CmdStanMCMC documentation:

Posterior summary statistics

Summaries from the posterior package

The $summary() method calls summarise_draws() from the posterior package. The first argument specifies the variables to summarize and any arguments after that are passed on to posterior::summarise_draws() to specify which summaries to compute, whether to use multiple cores, etc.

fit$summary()
fit$summary(variables = c("theta", "lp__"), "mean", "sd")

# use a formula to summarize arbitrary functions, e.g. Pr(theta <= 0.5)
fit$summary("theta", pr_lt_half = ~ mean(. <= 0.5))

# summarise all variables with default and additional summary measures
fit$summary(
  variables = NULL,
  posterior::default_summary_measures(),
  extra_quantiles = ~posterior::quantile2(., probs = c(.0275, .975))
)
  variable  mean median   sd  mad     q5   q95 rhat ess_bulk ess_tail
1     lp__ -7.30  -6.99 0.79 0.33 -8.927 -6.75    1     1769     1938
2    theta  0.25   0.24 0.12 0.12  0.078  0.48    1     1228     1521
  variable  mean   sd
1    theta  0.25 0.12
2     lp__ -7.30 0.79
  variable pr_lt_half
1    theta       0.96
  variable  mean median   sd  mad     q5   q95  q2.75 q97.5
1     lp__ -7.30  -6.99 0.79 0.33 -8.927 -6.75 -9.429 -6.75
2    theta  0.25   0.24 0.12 0.12  0.078  0.48  0.062  0.54

CmdStan’s stansummary utility

CmdStan itself provides a stansummary utility that can be called using the $cmdstan_summary() method. This method will print summaries but won’t return anything.

Posterior draws

Extracting draws

The $draws() method can be used to extract the posterior draws in formats provided by the posterior package. Here we demonstrate only the draws_array and draws_df formats, but the posterior package supports other useful formats as well.

# default is a 3-D draws_array object from the posterior package
# iterations x chains x variables
draws_arr <- fit$draws() # or format="array"
str(draws_arr)
 'draws_array' num [1:1000, 1:4, 1:2] -7.01 -7.89 -7.41 -6.75 -6.91 ...
 - attr(*, "dimnames")=List of 3
  ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
  ..$ chain    : chr [1:4] "1" "2" "3" "4"
  ..$ variable : chr [1:2] "lp__" "theta"
# draws x variables data frame
draws_df <- fit$draws(format = "df")
str(draws_df)
draws_df [4,000 × 5] (S3: draws_df/draws/tbl_df/tbl/data.frame)
 $ lp__      : num [1:4000] -7.01 -7.89 -7.41 -6.75 -6.91 ...
 $ theta     : num [1:4000] 0.168 0.461 0.409 0.249 0.185 ...
 $ .chain    : int [1:4000] 1 1 1 1 1 1 1 1 1 1 ...
 $ .iteration: int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
 $ .draw     : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
print(draws_df)
# A draws_df: 1000 iterations, 4 chains, and 2 variables
   lp__ theta
1  -7.0  0.17
2  -7.9  0.46
3  -7.4  0.41
4  -6.7  0.25
5  -6.9  0.18
6  -6.9  0.33
7  -7.2  0.15
8  -6.8  0.29
9  -6.8  0.24
10 -6.8  0.24
# ... with 3990 more draws
# ... hidden reserved variables {'.chain', '.iteration', '.draw'}

To convert an existing draws object to a different format use the posterior::as_draws_*() functions.

# this should be identical to draws_df created via draws(format = "df")
draws_df_2 <- as_draws_df(draws_arr)
identical(draws_df, draws_df_2)
[1] TRUE

In general, converting to a different draws format in this way will be slower than just setting the appropriate format initially in the call to the $draws() method, but in most cases the speed difference will be minor.

The vignette Working with Posteriors has more details on posterior draws, including how to reproduce the structured output RStan users are accustomed to getting from rstan::extract().

Plotting draws

Plotting posterior distributions is as easy as passing the object returned by the $draws() method directly to plotting functions in our bayesplot package.

mcmc_hist(fit$draws("theta"))

Sampler diagnostics

Extracting diagnostic values for each iteration and chain

The $sampler_diagnostics() method extracts the values of the sampler parameters (treedepth__, divergent__, etc.) in formats supported by the posterior package. The default is as a 3-D array (iteration x chain x variable).

# this is a draws_array object from the posterior package
str(fit$sampler_diagnostics())
 'draws_array' num [1:1000, 1:4, 1:6] 2 1 2 2 2 1 2 1 2 1 ...
 - attr(*, "dimnames")=List of 3
  ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
  ..$ chain    : chr [1:4] "1" "2" "3" "4"
  ..$ variable : chr [1:6] "treedepth__" "divergent__" "energy__" "accept_stat__" ...
# this is a draws_df object from the posterior package
str(fit$sampler_diagnostics(format = "df"))
draws_df [4,000 × 9] (S3: draws_df/draws/tbl_df/tbl/data.frame)
 $ treedepth__  : num [1:4000] 2 1 2 2 2 1 2 1 2 1 ...
 $ divergent__  : num [1:4000] 0 0 0 0 0 0 0 0 0 0 ...
 $ energy__     : num [1:4000] 8.95 8.77 7.87 7.64 6.93 ...
 $ accept_stat__: num [1:4000] 0.688 0.811 1 0.966 0.976 ...
 $ stepsize__   : num [1:4000] 0.905 0.905 0.905 0.905 0.905 ...
 $ n_leapfrog__ : num [1:4000] 3 3 3 3 3 3 3 3 3 3 ...
 $ .chain       : int [1:4000] 1 1 1 1 1 1 1 1 1 1 ...
 $ .iteration   : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...
 $ .draw        : int [1:4000] 1 2 3 4 5 6 7 8 9 10 ...

Sampler diagnostic warnings and summaries

The $diagnostic_summary() method will display any sampler diagnostic warnings and return a summary of diagnostics for each chain.

fit$diagnostic_summary()
$num_divergent
[1] 0 0 0 0

$num_max_treedepth
[1] 0 0 0 0

$ebfmi
[1] 1.11 0.76 1.19 1.08

We see the number of divergences for each of the four chains, the number of times the maximum treedepth was hit for each chain, and the E-BFMI for each chain.

In this case there were no warnings, so in order to demonstrate the warning messages we’ll use one of the CmdStanR example models that suffers from divergences.

fit_with_warning <- cmdstanr_example("schools")
Warning: 143 of 4000 (4.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.
Warning: 1 of 4 chains had an E-BFMI less than 0.3.
See https://mc-stan.org/misc/warnings for details.

After fitting there is a warning about divergences. We can also regenerate this warning message later using fit$diagnostic_summary().

diagnostics <- fit_with_warning$diagnostic_summary()
Warning: 143 of 4000 (4.0%) transitions ended with a divergence.
See https://mc-stan.org/misc/warnings for details.
Warning: 1 of 4 chains had an E-BFMI less than 0.3.
See https://mc-stan.org/misc/warnings for details.
print(diagnostics)
$num_divergent
[1]  1 37 75 30

$num_max_treedepth
[1] 0 0 0 0

$ebfmi
[1] 0.17 0.35 0.34 0.42
# number of divergences reported in warning is the sum of the per chain values
sum(diagnostics$num_divergent)
[1] 143

CmdStan’s diagnose utility

CmdStan itself provides a diagnose utility that can be called using the $cmdstan_diagnose() method. This method will print warnings but won’t return anything.

Running optimization and variational inference

CmdStanR also supports running Stan’s optimization algorithms and its algorithms for variational approximation of full Bayesian inference. These are run via the $optimize(), $laplace(), $variational(), and $pathfinder() methods, which are called in a similar way to the $sample() method demonstrated above.

Optimization

We can find the (penalized) maximum likelihood estimate (MLE) using $optimize().

fit_mle <- mod$optimize(data = data_list, seed = 123)
Initial log joint probability = -16.144 
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
       6      -5.00402   0.000246518   8.73164e-07           1           1        9    
Optimization terminated normally:  
  Convergence detected: relative gradient magnitude is below tolerance 
Finished in  0.2 seconds.
fit_mle$print() # includes lp__ (log prob calculated by Stan program)
 variable estimate
    lp__     -5.00
    theta     0.20
fit_mle$mle("theta")
theta 
  0.2

Here’s a plot comparing the penalized MLE to the posterior distribution of theta.

mcmc_hist(fit$draws("theta")) +
  vline_at(fit_mle$mle("theta"), size = 1.5)

For optimization, by default the mode is calculated without the Jacobian adjustment for constrained variables, which shifts the mode due to the change of variables. To include the Jacobian adjustment and obtain a maximum a posteriori (MAP) estimate set jacobian=TRUE. See the Maximum Likelihood Estimation section of the CmdStan User’s Guide for more details.

fit_map <- mod$optimize(
  data = data_list,
  jacobian = TRUE,
  seed = 123
)
Initial log joint probability = -18.2733 
    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes  
       5      -6.74802   0.000708195   1.43227e-05           1           1        8    
Optimization terminated normally:  
  Convergence detected: relative gradient magnitude is below tolerance 
Finished in  0.1 seconds.

Laplace Approximation

The $laplace() method produces a sample from a normal approximation centered at the mode of a distribution in the unconstrained space. If the mode is a MAP estimate, the samples provide an estimate of the mean and standard deviation of the posterior distribution. If the mode is the MLE, the sample provides an estimate of the standard error of the likelihood. Whether the mode is the MAP or MLE depends on the value of the jacobian argument when running optimization. See the Laplace Sampling chapter of the CmdStan User’s Guide for more details.

Here we pass in the fit_map object from above as the mode argument. If mode is omitted then optimization will be run internally before taking draws from the normal approximation.

fit_laplace <- mod$laplace(
    mode = fit_map,
    draws = 4000,
    data = data_list,
    seed = 123,
    refresh = 1000
  )
Calculating Hessian 
Calculating inverse of Cholesky factor 
Generating draws 
iteration: 0 
iteration: 1000 
iteration: 2000 
iteration: 3000 
Finished in  0.1 seconds.
fit_laplace$print("theta")
 variable mean median   sd  mad   q5  q95
    theta 0.27   0.25 0.12 0.12 0.10 0.51
mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025)

Variational (ADVI)

We can run Stan’s experimental Automatic Differentiation Variational Inference (ADVI) using the $variational() method. For details on the ADVI algorithm see the CmdStan User’s Guide.

fit_vb <- mod$variational(
  data = data_list,
  seed = 123,
  draws = 4000
)
------------------------------------------------------------ 
EXPERIMENTAL ALGORITHM: 
  This procedure has not been thoroughly tested and may be unstable 
  or buggy. The interface is subject to change. 
------------------------------------------------------------ 
Gradient evaluation took 9e-06 seconds 
1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds. 
Adjust your expectations accordingly! 
Begin eta adaptation. 
Iteration:   1 / 250 [  0%]  (Adaptation) 
Iteration:  50 / 250 [ 20%]  (Adaptation) 
Iteration: 100 / 250 [ 40%]  (Adaptation) 
Iteration: 150 / 250 [ 60%]  (Adaptation) 
Iteration: 200 / 250 [ 80%]  (Adaptation) 
Success! Found best value [eta = 1] earlier than expected. 
Begin stochastic gradient ascent. 
  iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes  
   100           -6.164             1.000            1.000 
   200           -6.225             0.505            1.000 
   300           -6.186             0.339            0.010   MEDIAN ELBO CONVERGED 
Drawing a sample of size 4000 from the approximate posterior...  
COMPLETED. 
Finished in  0.1 seconds.
fit_vb$print("theta")
 variable mean median   sd  mad   q5  q95
    theta 0.26   0.24 0.11 0.11 0.11 0.46
mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025)

Variational (Pathfinder)

Stan version 2.33 introduced a new variational method called Pathfinder, which is intended to be faster and more stable than ADVI. For details on how Pathfinder works see the section in the CmdStan User’s Guide. Pathfinder is run using the $pathfinder() method.

fit_pf <- mod$pathfinder(
  data = data_list,
  seed = 123,
  draws = 4000
)
Path [1] :Initial log joint density = -18.273334 
Path [1] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
              5      -6.748e+00      7.082e-04   1.432e-05    1.000e+00  1.000e+00       126 -6.145e+00 -6.145e+00                   
Path [1] :Best Iter: [5] ELBO (-6.145070) evaluations: (126) 
Path [2] :Initial log joint density = -19.192715 
Path [2] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
              5      -6.748e+00      2.015e-04   2.228e-06    1.000e+00  1.000e+00       126 -6.223e+00 -6.223e+00                   
Path [2] :Best Iter: [2] ELBO (-6.170358) evaluations: (126) 
Path [3] :Initial log joint density = -6.774820 
Path [3] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
              4      -6.748e+00      1.137e-04   2.596e-07    1.000e+00  1.000e+00       101 -6.178e+00 -6.178e+00                   
Path [3] :Best Iter: [4] ELBO (-6.177909) evaluations: (101) 
Path [4] :Initial log joint density = -7.949193 
Path [4] : Iter      log prob        ||dx||      ||grad||     alpha      alpha0      # evals       ELBO    Best ELBO        Notes  
              5      -6.748e+00      2.145e-04   1.301e-06    1.000e+00  1.000e+00       126 -6.197e+00 -6.197e+00                   
Path [4] :Best Iter: [5] ELBO (-6.197118) evaluations: (126) 
Total log probability function evaluations:4379 
Finished in  0.1 seconds.
fit_pf$print("theta")
 variable mean median   sd  mad   q5  q95
    theta 0.25   0.24 0.12 0.12 0.08 0.47

Let’s extract the draws, make the same plot we made after running the other algorithms, and compare them all. approximation, and compare them all. In this simple example the distributions are quite similar, but this will not always be the case for more challenging problems.

mcmc_hist(fit_pf$draws("theta"), binwidth = 0.025) +
  ggplot2::labs(subtitle = "Approximate posterior from pathfinder") +
  ggplot2::xlim(0, 1)

mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025) +
  ggplot2::labs(subtitle = "Approximate posterior from variational") +
  ggplot2::xlim(0, 1)

mcmc_hist(fit_laplace$draws("theta"), binwidth = 0.025) +
  ggplot2::labs(subtitle = "Approximate posterior from Laplace") +
  ggplot2::xlim(0, 1)

mcmc_hist(fit$draws("theta"), binwidth = 0.025) +
  ggplot2::labs(subtitle = "Posterior from MCMC") +
  ggplot2::xlim(0, 1)

For more details on the $optimize(), $laplace(), $variational(), and pathfinder() methods, follow these links to their documentation pages.

Saving fitted model objects

The $save_object() method provided by CmdStanR is the most convenient way to save a fitted model object to disk and ensure that all of the contents are available when reading the object back into R.

fit$save_object(file = "fit.RDS")

# can be read back in using readRDS
fit2 <- readRDS("fit.RDS")

But if your model object is large, then $save_object() could take a long time. $save_object() reads the CmdStan results files into memory, stores them in the model object, and saves the object with saveRDS(). To speed up the process, you can emulate $save_object() and replace saveRDS with the much faster qsave() function from the qs package.

# Load CmdStan output files into the fitted model object.
fit$draws() # Load posterior draws into the object.
try(fit$sampler_diagnostics(), silent = TRUE) # Load sampler diagnostics.
try(fit$init(), silent = TRUE) # Load user-defined initial values.
try(fit$profiles(), silent = TRUE) # Load profiling samples.

# Save the object to a file.
qs::qsave(x = fit, file = "fit.qs")

# Read the object.
fit2 <- qs::qread("fit.qs")

Storage is even faster if you discard results you do not need to save. The following example saves only posterior draws and discards sampler diagnostics, user-specified initial values, and profiling data.

# Load posterior draws into the fitted model object and omit other output.
fit$draws()

# Save the object to a file.
qs::qsave(x = fit, file = "fit.qs")

# Read the object.
fit2 <- qs::qread("fit.qs")

See the vignette How does CmdStanR work? for more information about the composition of CmdStanR objects.

Comparison with RStan

Different ways of interfacing with Stan’s C++

The RStan interface (rstan package) is an in-memory interface to Stan and relies on R packages like Rcpp and inline to call C++ code from R. On the other hand, the CmdStanR interface does not directly call any C++ code from R, instead relying on the CmdStan interface behind the scenes for compilation, running algorithms, and writing results to output files.

Advantages of RStan

  • Allows other developers to distribute R packages with pre-compiled Stan programs (like rstanarm) on CRAN. (Note: As of 2023, this can mostly be achieved with CmdStanR as well. See Developing using CmdStanR.)

  • Avoids use of R6 classes, which may result in more familiar syntax for many R users.

  • CRAN binaries available for Mac and Windows.

Advantages of CmdStanR

  • Compatible with latest versions of Stan. Keeping up with Stan releases is complicated for RStan, often requiring non-trivial changes to the rstan package and new CRAN releases of both rstan and StanHeaders. With CmdStanR the latest improvements in Stan will be available from R immediately after updating CmdStan using cmdstanr::install_cmdstan().

  • Running Stan via external processes results in fewer unexpected crashes, especially in RStudio.

  • Less memory overhead.

  • More permissive license. RStan uses the GPL-3 license while the license for CmdStanR is BSD-3, which is a bit more permissive and is the same license used for CmdStan and the Stan C++ source code.

Additional resources

There are additional vignettes available that discuss other aspects of using CmdStanR. These can be found online at the CmdStanR website:

To ask a question please post on the Stan forums:

To report a bug, suggest a feature (including additions to these vignettes), or to start contributing to CmdStanR development (new contributors welcome!) please open an issue on GitHub: