## Introduction

CmdStanR is a lightweight interface to Stan for R users (see CmdStanPy for Python) 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.

CmdStanR is not on CRAN yet, but the beta release can be installed 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://mc-stan.org/r-packages/", getOption("repos")))

CmdStanR (the cmdstanr R package) can now be loaded like any other R package. We’ll also load the bayesplot and posterior packages to use later in examples.

library(cmdstanr)
library(posterior)
library(bayesplot)
color_scheme_set("brightblue")

## 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:

check_cmdstan_toolchain()
The CmdStan toolchain 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 ".cmdstanr/cmdstan-[version]", for example ".cmdstanr/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():

cmdstan_path()
[1] "/Users/jgabry/.cmdstanr/cmdstan-2.25.0"
cmdstan_version()
[1] "2.25.0"

## 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)
Model executable is up to date!

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; int<lower=0,upper=1> y[N]; } 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/.cmdstanr/cmdstan-2.25.0/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 = 2, refresh = 500 ) Running MCMC with 4 chains, at most 2 in parallel... 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 1 finished in 0.0 seconds. Chain 2 finished in 0.0 seconds. 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 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 The $summary() method calls summarise_draws() from the posterior package:

fit$summary() # A tibble: 2 x 10 variable mean median sd mad q5 q95 rhat ess_bulk ess_tail <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 lp__ -7.28 -6.99 0.764 0.328 -8.79 -6.75 1.00 1610. 1517. 2 theta 0.252 0.236 0.121 0.121 0.0804 0.476 1.00 1467. 1356. fit$summary("theta", "mean", "sd")
# A tibble: 1 x 3
variable  mean    sd
<chr>    <dbl> <dbl>
1 theta    0.252 0.121
# use a formula to summarize arbitrary functions, e.g. Pr(theta <= 0.5)
fit$summary("theta", pr_lt_half = ~ mean(. <= 0.5)) # A tibble: 1 x 2 variable pr_lt_half <chr> <dbl> 1 theta 0.965 ### Posterior draws The $draws() method can be used to extract the posterior draws as a 3-D array (iteration x chain x variable). The posterior package can then be used to easily convert to other formats, like data frames and matrices of draws.

# this is a draws_array object from the posterior package
draws_array <- fit$draws() str(draws_array)  'draws_array' num [1:1000, 1:4, 1:2] -6.82 -7.32 -7.05 -7.13 -7.11 ... - attr(*, "dimnames")=List of 3 ..$ iteration: chr [1:1000] "1" "2" "3" "4" ...
..$chain : chr [1:4] "1" "2" "3" "4" ..$ variable : chr [1:2] "lp__" "theta"
# convert to matrix or data frame
draws_df <- as_draws_df(draws_array) # as_draws_matrix() for matrix
print(draws_df)
# A draws_df: 1000 iterations, 4 chains, and 2 variables
lp__ theta
1  -6.8  0.30
2  -7.3  0.13
3  -7.1  0.16
4  -7.1  0.37
5  -7.1  0.15
6  -7.5  0.12
7  -7.2  0.38
8  -6.8  0.22
9  -7.0  0.34
10 -6.8  0.22
# ... with 3990 more draws
# ... hidden reserved variables {'.chain', '.iteration', '.draw'}

Plotting posterior distributions is as easy as passing the object returned by the $draws() method directly to plotting functions in bayesplot: mcmc_hist(fit$draws("theta"))

### Sampler diagnostics

The $sampler_diagnostics() method extracts the values of the sampler parameters (treedepth__, divergent__, etc.) as a 3-D array (iteration x chain x variable): # this is a draws_array object from the posterior package str(fit$sampler_diagnostics())
 'draws_array' num [1:1000, 1:4, 1:6] 1 0.801 0.989 0.92 1 ...
- attr(*, "dimnames")=List of 3
..$iteration: chr [1:1000] "1" "2" "3" "4" ... ..$ chain    : chr [1:4] "1" "2" "3" "4"
..$variable : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ... # convert to matrix or data frame using posterior package diagnostics_df <- as_draws_df(fit$sampler_diagnostics())
print(diagnostics_df)
# A draws_df: 1000 iterations, 4 chains, and 6 variables
accept_stat__ stepsize__ treedepth__ n_leapfrog__ divergent__ energy__
1           1.00       0.93           1            1           0      6.9
2           0.80       0.93           1            3           0      8.1
3           0.99       0.93           2            3           0      7.5
4           0.92       0.93           1            3           0      8.1
5           1.00       0.93           2            7           0      7.1
6           0.95       0.93           2            3           0      7.8
7           0.88       0.93           1            3           0      9.3
8           1.00       0.93           2            3           0      7.1
9           0.95       0.93           1            3           0      7.2
10          0.97       0.93           1            3           0      7.2
# ... with 3990 more draws
# ... hidden reserved variables {'.chain', '.iteration', '.draw'}

### CmdStan utilities

The $cmdstan_diagnose() and $cmdstan_summary() methods call CmdStan’s diagnose and stansummary utilities:

fit$cmdstan_diagnose() Processing csv files: /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpnnYljv/bernoulli-202012171341-1-2e6207.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpnnYljv/bernoulli-202012171341-2-2e6207.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpnnYljv/bernoulli-202012171341-3-2e6207.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpnnYljv/bernoulli-202012171341-4-2e6207.csv Checking sampler transitions treedepth. Treedepth satisfactory for all transitions. Checking sampler transitions for divergences. No divergent transitions found. Checking E-BFMI - sampler transitions HMC potential energy. E-BFMI satisfactory for all transitions. Effective sample size satisfactory. Split R-hat values satisfactory all parameters. Processing complete, no problems detected. fit$cmdstan_summary()
Input files: /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpnnYljv/bernoulli-202012171341-1-2e6207.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpnnYljv/bernoulli-202012171341-2-2e6207.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpnnYljv/bernoulli-202012171341-3-2e6207.csv, /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpnnYljv/bernoulli-202012171341-4-2e6207.csv
Inference for Stan model: bernoulli_model
4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.

Warmup took (0.0070, 0.0060, 0.0070, 0.0060) seconds, 0.026 seconds total
Sampling took (0.017, 0.017, 0.015, 0.016) seconds, 0.065 seconds total

Mean     MCSE  StdDev     5%   50%   95%    N_Eff  N_Eff/s    R_hat

lp__            -7.3  2.0e-02    0.76   -8.8  -7.0  -6.8     1431    22012      1.0
accept_stat__   0.90  2.5e-03    0.15   0.57  0.97   1.0  3.7e+03  5.7e+04  1.0e+00
stepsize__       1.1  8.8e-02    0.12   0.93   1.2   1.3  2.0e+00  3.1e+01  3.5e+13
treedepth__      1.4  8.2e-03    0.51    1.0   1.0   2.0  3.8e+03  5.8e+04  1.0e+00
n_leapfrog__     2.5  1.9e-01     1.3    1.0   3.0   3.0  4.3e+01  6.7e+02  1.0e+00
divergent__     0.00      nan    0.00   0.00  0.00  0.00      nan      nan      nan
energy__         7.8  2.7e-02     1.0    6.8   7.4   9.9  1.5e+03  2.3e+04  1.0e+00

theta           0.25  3.1e-03    0.12  0.080  0.24  0.48     1542    23728      1.0

Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at
convergence, R_hat=1).

### Create a stanfit object

If you have RStan installed then it is also possible to create a stanfit object from the csv output files written by CmdStan. This can be done by using rstan::read_stan_csv() in combination with the $output_files() method of the CmdStanMCMC object: stanfit <- rstan::read_stan_csv(fit$output_files())

## 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() and $variational() 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$optimize(data = data_list, seed = 123) Initial log joint probability = -9.51104 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 6 -5.00402 0.000103557 2.55661e-07 1 1 9 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Finished in 0.1 seconds. fit_mle$summary() # includes lp__ (log prob calculated by Stan program)
# A tibble: 2 x 2
variable estimate
<chr>       <dbl>
1 lp__        -5.00
2 theta        0.2 
fit_mle$mle("theta") theta 0.2  Here’s a plot comparing the MLE to the posterior distribution of theta: mcmc_hist(fit$draws("theta")) +
vline_at(fit_mle$mle(), size = 1.5) ### Variational Bayes We can run Stan’s experimental variational Bayes algorithm using the $variational() method:

fit_vb <- mod$variational(data = data_list, seed = 123, output_samples = 4000) ------------------------------------------------------------ EXPERIMENTAL ALGORITHM: This procedure has not been thoroughly tested and may be unstable or buggy. The interface is subject to change. ------------------------------------------------------------ Gradient evaluation took 1.1e-05 seconds 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds. Adjust your expectations accordingly! Begin eta adaptation. Iteration: 1 / 250 [ 0%] (Adaptation) Iteration: 50 / 250 [ 20%] (Adaptation) Iteration: 100 / 250 [ 40%] (Adaptation) Iteration: 150 / 250 [ 60%] (Adaptation) Iteration: 200 / 250 [ 80%] (Adaptation) Success! Found best value [eta = 1] earlier than expected. Begin stochastic gradient ascent. iter ELBO delta_ELBO_mean delta_ELBO_med notes 100 -6.262 1.000 1.000 200 -6.263 0.500 1.000 300 -6.307 0.336 0.007 MEDIAN ELBO CONVERGED Drawing a sample of size 4000 from the approximate posterior... COMPLETED. Finished in 0.1 seconds. fit_vb$summary("theta")
# A tibble: 1 x 7
variable  mean median    sd   mad    q5   q95
<chr>    <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
1 theta    0.267  0.250 0.117 0.117 0.105 0.487

The $draws() method can be used to access the approximate posterior draws. Let’s extract the draws, make the same plot we made after MCMC, and compare the two. In this trivial example the distributions look quite similar, although the variational approximation slightly underestimates the posterior standard deviation: bayesplot_grid( mcmc_hist(fit$draws("theta"), binwidth = 0.025),
mcmc_hist(fit_vb$draws("theta"), binwidth = 0.025), titles = c("Posterior distribution from MCMC", "Approximate posterior from VB"), xlim = c(0, 1) ) For more details on the $optimize() and $variational() methods, follow these thinks to their documentation pages: ## Saving fitted model objects In order to save a fitted model object to disk and ensure that all of the contents are available when reading the object back into R, we recommend using the $save_object() method provided by CmdStanR. The reason for this is discussed in detail in the vignette How does CmdStanR work?, so here we just demonstrate how to use the method:

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

fit2 <- readRDS("fit.RDS")
Both forms of interfacing with Stan have advantages and disadvantages. An in-memory interface like RStan is able to offer more advanced features than CmdStanR (for example the rstan::log_prob() and rstan::grad_log_prob() methods) but keeping up with Stan releases is more complicated for RStan, often requiring non-trivial changes to the rstan package and requiring new CRAN releases of rstan and StanHeaders. On the other hand, with CmdStanR, the latest features in Stan will be available from R immediately after updating CmdStan, without an update to the cmdstanr package. We also anticipate that running Stan via external processes will have the advantage of playing nicer with R (and RStudio) and result in fewer unexpected crashes than when using RStan.