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Become a sponsor to Stan

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.

Users specify log density functions in Stan’s probabilistic programming language and get:

  • 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 is a C++ package built on top of the Stan Math library, which provides

  • a full first- and higher-order automatic differentiation library based on C++ template overloads, and
  • a supporting fully-templated matrix, linear algebra, and probability special function library.

There are interfaces available in R, Python, MATLAB, Julia, Stata, Mathematica, and for the command line.

Stan has a highly active development program with many volunteer contributors.

Stan Sponsorship

Funds contributed to the Stan project will be used for development, documentation and distribution of Stan software, and for technical and educational support for Stan users.

7 sponsors are funding stan-dev’s work.

@microprediction
@JWiley
@hoxo-m
@seabbs
@ysaito8015
@ncx-co
@github

Featured work

  1. stan-dev/rstan

    RStan, the R interface to Stan

    C++ 867
  2. stan-dev/pystan

    PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

    Python 193
  3. stan-dev/cmdstan

    CmdStan, the command line interface to Stan

    C++ 154
  4. stan-dev/stan

    Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

  5. stan-dev/rstanarm

    rstanarm R package for Bayesian applied regression modeling

  6. stan-dev/math

    The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic model…

    C++ 599

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