Stan Development Team
CmdStanR: the R interface to CmdStan.
CmdStanR (cmdstanr package) is an interface to Stan (mc-stan.org) for R users. It provides the necessary objects and functions to compile a Stan program and run Stan's algorithms from R via CmdStan, the shell interface to Stan (mc-stan.org/users/interfaces/cmdstan).
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.
Allows other developers to distribute R packages with pre-compiled Stan programs (like rstanarm) on CRAN.
Avoids use of R6 classes, which may result in more familiar syntax for many R users.
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.
CmdStanR requires a working version of CmdStan. If
you already have CmdStan installed see cmdstan_model()
to get started,
otherwise see install_cmdstan()
to install CmdStan. The vignette
Getting started with CmdStanR
demonstrates the basic functionality of the package.
The CmdStanR website (mc-stan.org/cmdstanr) for online documentation and tutorials.
The Stan and CmdStan documentation:
Stan documentation: mc-stan.org/users/documentation
CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide
# \dontrun{
library(cmdstanr)
library(posterior)
library(bayesplot)
color_scheme_set("brightblue")
# Set path to CmdStan
# (Note: if you installed CmdStan via install_cmdstan() with default settings
# then setting the path is unnecessary but the default below should still work.
# Otherwise use the `path` argument to specify the location of your
# CmdStan installation.)
set_cmdstan_path(path = NULL)
#> CmdStan path set to: /Users/jgabry/.cmdstan/cmdstan-2.32.2
# Create a CmdStanModel object from a Stan program,
# here using the example model that comes with CmdStan
file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan")
mod <- cmdstan_model(file)
#> Model executable is up to date!
mod$print()
#> data {
#> int<lower=0> N;
#> array[N] int<lower=0,upper=1> y;
#> }
#> parameters {
#> real<lower=0,upper=1> theta;
#> }
#> model {
#> theta ~ beta(1,1); // uniform prior on interval 0,1
#> y ~ bernoulli(theta);
#> }
# Data as a named list (like RStan)
stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
# Run MCMC using the 'sample' method
fit_mcmc <- mod$sample(
data = stan_data,
seed = 123,
chains = 2,
parallel_chains = 2
)
#> Running MCMC with 2 parallel chains...
#>
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#> Chain 1 finished in 0.0 seconds.
#> Chain 2 finished in 0.0 seconds.
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
#> Total execution time: 0.2 seconds.
#>
# Use 'posterior' package for summaries
fit_mcmc$summary()
#> # A tibble: 2 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1 lp__ -7.30 -7.03 0.721 0.380 -8.82 -6.75 1.00 902. 1006.
#> 2 theta 0.247 0.233 0.122 0.129 0.0786 0.470 1.00 762. 712.
# Get posterior draws
draws <- fit_mcmc$draws()
print(draws)
#> # A draws_array: 1000 iterations, 2 chains, and 2 variables
#> , , variable = lp__
#>
#> chain
#> iteration 1 2
#> 1 -6.8 -6.8
#> 2 -6.9 -6.8
#> 3 -7.0 -7.0
#> 4 -6.9 -7.1
#> 5 -6.7 -7.0
#>
#> , , variable = theta
#>
#> chain
#> iteration 1 2
#> 1 0.28 0.21
#> 2 0.19 0.20
#> 3 0.16 0.17
#> 4 0.20 0.36
#> 5 0.25 0.34
#>
#> # ... with 995 more iterations
# Convert to data frame using posterior::as_draws_df
as_draws_df(draws)
#> # A draws_df: 1000 iterations, 2 chains, and 2 variables
#> lp__ theta
#> 1 -6.8 0.28
#> 2 -6.9 0.19
#> 3 -7.0 0.16
#> 4 -6.9 0.20
#> 5 -6.7 0.25
#> 6 -7.1 0.36
#> 7 -9.0 0.55
#> 8 -7.2 0.15
#> 9 -6.8 0.23
#> 10 -7.5 0.42
#> # ... with 1990 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
# Plot posterior using bayesplot (ggplot2)
mcmc_hist(fit_mcmc$draws("theta"))
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Call CmdStan's diagnose and stansummary utilities
fit_mcmc$cmdstan_diagnose()
#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp9Z3PNu/bernoulli-202307271729-1-67e85d.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/Rtmp9Z3PNu/bernoulli-202307271729-2-67e85d.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.
#>
#> Effective sample size satisfactory.
#>
#> Split R-hat values satisfactory all parameters.
#>
#> Processing complete, no problems detected.
fit_mcmc$cmdstan_summary()
#> Inference for Stan model: bernoulli_model
#> 2 chains: each with iter=(1000,1000); warmup=(0,0); thin=(1,1); 2000 iterations saved.
#>
#> Warmup took (0.0040, 0.0030) seconds, 0.0070 seconds total
#> Sampling took (0.011, 0.011) seconds, 0.022 seconds total
#>
#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
#>
#> lp__ -7.3 2.6e-02 0.72 -8.8 -7.0 -6.8 781 35502 1.0
#> accept_stat__ 0.92 8.3e-03 0.13 0.64 0.97 1.0 235 10662 1.0e+00
#> stepsize__ 0.95 7.9e-02 0.079 0.87 1.0 1.0 1.0 46 2.0e+13
#> treedepth__ 1.4 1.1e-02 0.48 1.0 1.0 2.0 1874 85179 1.0e+00
#> n_leapfrog__ 2.5 1.4e-01 1.3 1.0 3.0 3.0 89 4050 1.0e+00
#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan
#> energy__ 7.8 3.6e-02 1.00 6.8 7.5 9.6 775 35215 1.0e+00
#>
#> theta 0.25 4.3e-03 0.12 0.079 0.23 0.47 796 36197 1.0
#>
#> Samples were drawn using hmc with nuts.
#> For each parameter, N_Eff is a crude measure of effective sample size,
#> and R_hat is the potential scale reduction factor on split chains (at
#> convergence, R_hat=1).
# For models fit using MCMC, if you like working with RStan's stanfit objects
# then you can create one with rstan::read_stan_csv()
# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())
# Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm)
# and also demonstrate specifying data as a path to a file instead of a list
my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json")
fit_optim <- mod$optimize(data = my_data_file, seed = 123)
#> Initial log joint probability = -9.51104
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
#> 6 -5.00402 0.000103557 2.55661e-07 1 1 9
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
fit_optim$summary()
#> # A tibble: 2 × 2
#> variable estimate
#> <chr> <num>
#> 1 lp__ -5.00
#> 2 theta 0.2
# Run 'variational' method to approximate the posterior (default is meanfield ADVI)
fit_vb <- mod$variational(data = stan_data, seed = 123)
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 5e-06 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 100 -6.262 1.000 1.000
#> 200 -6.263 0.500 1.000
#> 300 -6.307 0.336 0.007 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 0.1 seconds.
fit_vb$summary()
#> # A tibble: 3 × 7
#> variable mean median sd mad q5 q95
#> <chr> <num> <num> <num> <num> <num> <num>
#> 1 lp__ -7.18 -6.94 0.588 0.259 -8.36 -6.75
#> 2 lp_approx__ -0.515 -0.221 0.692 0.303 -2.06 -0.00257
#> 3 theta 0.263 0.246 0.115 0.113 0.106 0.481
# Plot approximate posterior using bayesplot
mcmc_hist(fit_vb$draws("theta"))
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
data = stan_data,
seed = 123,
chains = 2,
refresh = 0,
init = function() list(theta = runif(1))
)
#> Running MCMC with 2 sequential chains...
#>
#> Chain 1 finished in 0.0 seconds.
#> Chain 2 finished in 0.0 seconds.
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
#> Total execution time: 0.3 seconds.
#>
fit_mcmc_w_init_fun_2 <- mod$sample(
data = stan_data,
seed = 123,
chains = 2,
refresh = 0,
init = function(chain_id) {
# silly but demonstrates optional use of chain_id
list(theta = 1 / (chain_id + 1))
}
)
#> Running MCMC with 2 sequential chains...
#>
#> Chain 1 finished in 0.0 seconds.
#> Chain 2 finished in 0.0 seconds.
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
#> Total execution time: 0.3 seconds.
#>
fit_mcmc_w_init_fun_2$init()
#> [[1]]
#> [[1]]$theta
#> [1] 0.5
#>
#>
#> [[2]]
#> [[2]]$theta
#> [1] 0.3333333
#>
#>
# Specifying initial values as a list of lists
fit_mcmc_w_init_list <- mod$sample(
data = stan_data,
seed = 123,
chains = 2,
refresh = 0,
init = list(
list(theta = 0.75), # chain 1
list(theta = 0.25) # chain 2
)
)
#> Running MCMC with 2 sequential chains...
#>
#> Chain 1 finished in 0.0 seconds.
#> Chain 2 finished in 0.0 seconds.
#>
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
#> Total execution time: 0.3 seconds.
#>
fit_optim_w_init_list <- mod$optimize(
data = stan_data,
seed = 123,
init = list(
list(theta = 0.75)
)
)
#> Initial log joint probability = -11.6657
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
#> 6 -5.00402 0.000237915 9.55309e-07 1 1 9
#> Optimization terminated normally:
#> Convergence detected: relative gradient magnitude is below tolerance
#> Finished in 0.1 seconds.
fit_optim_w_init_list$init()
#> [[1]]
#> [[1]]$theta
#> [1] 0.75
#>
#>
# }