CmdStan.jl v6 provides a Julia wrapper to Stan's `cmdstan` executable.
Author StanJulia
22 Stars
Updated Last
11 Months Ago
Started In
May 2018


A package to run Stan's cmdstan executable from Julia.

Documentation Build Status

CmdStan.jl stargazers over time

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Stan.jl stargazers over time

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For more info on Stan, please go to

The Julia package CmdStan.jl is based on Stan's command line interface, 'cmdstan'.

The 'cmdstan' interface needs to be installed separatedly. Please see cmdstan installation for further details.

The location of the cmdstan executable and related programs is now obtained from the environment variable JULIA_CMDSTAN_HOME. This used to be CMDSTAN_HOME.

Right now versioninfo() will show its setting (if defined).


Release 6.0.2

  1. Init files were not properly included in cmd. Thanks to ueliwechsler and andrstef.

Release 6.0.1

  1. Removed dependency on Documenter.

Release 6.0.0 contains:

  1. Revert back to output an array by default.
  2. Switch to Requires.jl to delay/prevent loading of MCMCChains if not needed (thanks to suggestions by @Byrth and Stijn de Waele).
  3. Updates to documentation.

Release 6.0.0 is a breaking release.

To revert back to v5.x behavior a script needs to include using MCMCChains (which thus must be installed) and specify output_format=:mcmcchains in the call to stanmodel(). This option is not tested on Travis. A sub-directory examples_mcmcchains has been added which demonstrate this usage pattern.

CmdStan.jl tested on cmdstan v2.21.0.


  • STABLEdocumentation of the most recently tagged version.
  • DEVELdocumentation of the in-development version.

Questions and issues

Question and contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems or have a question.


There is no shortage of good books on Bayesian statistics. A few of my favorites are:

  1. Bolstad: Introduction to Bayesian statistics

  2. Bolstad: Understanding Computational Bayesian Statistics

  3. Gelman, Hill: Data Analysis using regression and multileve,/hierachical models

  4. McElreath: Statistical Rethinking

  5. Gelman, Carlin, and others: Bayesian Data Analysis

  6. Lee, Wagenmakers: Bayesian Cognitive Modeling

  7. Kruschke:Doing Bayesian Data Analysis

and a great read (and implemented in DynamicHMC.jl):

  1. Betancourt: A Conceptual Introduction to Hamiltonian Monte Carlo

CmdStan.jl and several other Julia based mcmc packages are used in StatisticalRethinking.jl