rstan

RStan is the R interface to the Stan probabilistic programming language, enabling Bayesian inference through the No-U-Turn Sampler (NUTS), a variant of Hamiltonian Monte Carlo.

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RStan is the R interface to the Stan probabilistic programming language, enabling Bayesian inference through the No-U-Turn Sampler (NUTS), a variant of Hamiltonian Monte Carlo. It is designed for statisticians, data scientists, and researchers who need to specify custom statistical models beyond what pre-packaged routines offer. Unlike higher-level interfaces like brms or rstanarm, RStan requires users to write Stan code directly, granting full control over model specification—essential for complex hierarchical models, non-standard likelihoods, or custom priors.

The package is part of the Stan ecosystem, which also includes CmdStanR (a newer command-line interface) and the pre-compiled rstanarm. RStan is licensed under GPLv3, with the underlying Stan code under new BSD. It is maintained by the Stan Development Team and hosted on GitHub (stan-dev/rstan).

Installation requires a C++ toolchain (e.g., Rtools on Windows), and the package compiles models on first use—a trade-off for flexibility versus the immediate execution of rstanarm's pre-compiled models. RStan is best suited for users who need to push beyond the constraints of formula-based interfaces and are comfortable writing Stan's C-like syntax for defining parameters, transformed parameters, and likelihoods.

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How it works

  1. No-U-Turn Sampler (NUTS)

    Implements adaptive Hamiltonian Monte Carlo for efficient posterior sampling, automatically tuning step size and trajectory length.

  2. Custom Stan code

    Users write full Stan programs, enabling any model Stan can represent—including non-linear, latent variable, and differential equation models.

  3. R integration

    Provides R functions (stan(), sampling()) to compile and run Stan models, returning stanfit objects for post-processing in R.

  4. Diagnostics and convergence

    Outputs Rhat, effective sample size, and divergent transition counts to assess MCMC convergence and sampling quality.

  5. Cross-platform support

    Works on Windows, macOS, and Linux, with installation guides in English, French, Japanese, Traditional Chinese, and Portuguese.

  6. Open-source licensing

    RStan is GPLv3; the embedded Stan library is new BSD, allowing free use, modification, and redistribution.

  7. Stan ecosystem compatibility

    Interoperates with CmdStanR, brms, rstanarm, and the Stan Math library, though each has distinct workflows and compilation requirements.

Strengths and trade-offs

Strengths

  • RStan allows fitting any model Stan can express, including complex hierarchical, non-linear, and custom likelihood models that brms or rstanarm cannot handle.
  • The No-U-Turn Sampler (NUTS) automatically tunes Hamiltonian Monte Carlo parameters, reducing the need for manual tuning and improving sampling efficiency.
  • RStan provides detailed convergence diagnostics (Rhat, effective sample size, divergent transitions) directly in the stanfit object, enabling robust model checking.
  • The package is free and open-source (GPLv3), with extensive documentation, vignettes, and an active community forum (discourse.mc-stan.org) for support.

Trade-offs

  • RStan requires users to write Stan code manually, which has a steep learning curve compared to formula-based interfaces like brms or rstanarm.
  • Models must be compiled on first use via a C++ toolchain (e.g., Rtools on Windows), adding setup time and complexity, unlike rstanarm's pre-compiled models.
  • RStan does not support the latest Stan features (e.g., GPU acceleration, parallel chains via MPI) as well as CmdStanR, which is the recommended interface for new users.
  • The package is primarily designed for offline Bayesian modeling and lacks built-in support for automated workflow features like model comparison or posterior predictive checks.

Pricing context

Free and open-source (GPLv3). No paid tiers or subscription plans.

Getting started with rstan

  1. Install RStan

    Install RStan by running install.packages("rstan", dependencies = TRUE) in R. On Windows, first install Rtools from https://cran.r-project.org/bin/windows/Rtools/ and ensure it is on your PATH.

  2. Load the rstan package

    Load the rstan package into your R session using library(rstan). This activates the Stan interface and makes functions like stan() and sampling() available for model compilation and inference.

  3. Write a Stan model

    Write your custom Stan model as a character string or in a .stan file. Define the data, parameters, transformed parameters, and model blocks using Stan's C-like syntax. For example, specify a simple linear regression with normal likelihood.

  4. Run MCMC sampling

    Call the stan() function with your model code and data list. Set the number of chains, iterations, and warmup. For example: fit <- stan(model_code = model_code, data = my_data, chains = 4, iter = 2000).

  5. Check convergence diagnostics

    Print the stanfit object to view Rhat values, effective sample sizes, and divergent transition counts. Use summary(fit) or extract(fit) to examine posterior summaries and ensure all Rhat values are below 1.05.

Frequently Asked Questions

What is RStan and how does it work for Bayesian inference?

RStan is the R interface to the Stan probabilistic programming language. It performs Bayesian inference using the No-U-Turn Sampler, a variant of Hamiltonian Monte Carlo, to efficiently sample from posterior distributions for custom statistical models.

How do I install RStan on Windows?

Installing RStan on Windows requires a C++ toolchain like Rtools. You then install the rstan package from CRAN. Models compile on first use, so setup time is longer compared to pre-compiled alternatives like rstanarm.

What are the main differences between RStan and brms?

RStan requires writing Stan code directly, giving full control over model specification for complex hierarchical models. brms uses formula syntax, making it easier but less flexible. RStan is suited for custom likelihoods and non-standard models beyond brms capabilities.

Is RStan free to use and what is its license?

Yes, RStan is free and open-source under the GPLv3 license. The embedded Stan library uses the new BSD license, allowing free use, modification, and redistribution. There are no paid tiers or subscription plans.

What convergence diagnostics does RStan provide?

RStan outputs Rhat, effective sample size, and divergent transition counts directly in the stanfit object. These diagnostics help assess MCMC convergence and sampling quality, enabling robust model checking without additional software.

What are the weaknesses of using RStan compared to CmdStanR?

RStan does not support the latest Stan features like GPU acceleration or parallel chains via MPI as well as CmdStanR. It also requires manual Stan code and compilation, making it less suitable for beginners compared to formula-based interfaces.

Alternatives

How rstan compares

Direct head-to-head against 2 competitors. Picked by 7wData.

This tool

rstan

Pricing
Free and open-source (GPLv3). No paid tiers or subscription plans.
Target
RStan is the R interface to the Stan probabilistic programming language, enabling Bayesian inference through the No-U-Turn Sampler (NUTS), a variant of Hamiltonian Monte Carlo.
Strength
RStan allows fitting any model Stan can express, including complex hierarchical, non-linear, and custom likelihood models that brms or rstanarm cannot handle.
Watch for
RStan requires users to write Stan code manually, which has a steep learning curve compared to formula-based interfaces like brms or rstanarm.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. mc-stan.org
  2. discourse.mc-stan.org
  3. github.com
  4. cran.r-project.org