Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.

Stan interfaces with the most popular data analysis languages (R, Python, shell, MATLAB, Julia, Stata) and runs on all major platforms (Linux, Mac, Windows). To get started using Stan begin with the Installation and Documentation pages.

Users specify log density functions in the Stan probabilistic programming language and then fit the models to data using:

  • full Bayesian statistical inference with MCMC sampling (NUTS, HMC)

  • approximate Bayesian inference with variational inference (ADVI)

  • penalized maximum likelihood estimation with optimization (L-BFGS)

Stan’s math library provides differentiable probability functions & linear algebra (C++ autodiff). Additional R packages provide expression-based linear modeling, posterior visualization, and leave-one-out cross-validation.

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