# 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.

Repository: https://github.com/stan-dev/stan
Canonical: https://ross.abutalabs.com/products/stan
Homepage: https://mc-stan.org
Language: C++
License: BSD-3-Clause
License Family: permissive
Topics: stan, bayesian-inference, bayesian, bayesian-methods, bayesian-statistics, bayesian-data-analysis
Last push: 2026-08-26T07:16:50+00:00
Link (homepage): https://mc-stan.org
Link (site_page): https://mc-stan.org/about/index.html
Link (site_page): https://mc-stan.org/install
Link (site_page): https://mc-stan.org/docs
Link (site_page): https://mc-stan.org/install/index.html

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 60, longevity 100
- inputs: {"age_days": 4956, "days_push": 7, "days_rel": 106, "gap_med": 132, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2761, forks 387 (observed 2026-08-28T04:07:18.489327+00:00)

## What it is
Stan is a C++ probabilistic programming library for full Bayesian inference via NUTS/Hamiltonian Monte Carlo, approximate inference via ADVI, and penalized maximum likelihood via L-BFGS. It is built on the Stan Math automatic differentiation library and offers interfaces from R, Python, Julia, and the command line.

## Use cases
- fit bayesian hierarchical models to data
- run MCMC sampling on a custom statistical model
- do variational inference for approximate posteriors
- estimate model parameters with maximum likelihood
- write probabilistic models in a declarative modeling language
- fit time-series or multilevel regression models

## When to choose
- you need full Bayesian posterior inference with state-of-the-art HMC sampling
- you want a mature, well-documented probabilistic programming language with multi-language interfaces
- you need automatic differentiation for custom statistical models
- you are doing academic or production statistical modeling in R, Python, or Julia

## When to avoid
- you only need simple frequentist regression available in base statistical packages
- you cannot install a C++17 toolchain
- you need deep learning or GPU-heavy neural network training rather than statistical inference
- you want a pure-Python or pure-R solution without compiled dependencies

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, math, simulation
- domain: machine-learning, data-science
- platform: cross-platform, cpp, python, jvm, cli
- tags: bayesian-inference, probabilistic-programming, mcmc, hamiltonian-monte-carlo, variational-inference, automatic-differentiation, statistical-modeling, statistics

## Member repositories
- stan-dev/stan (main) score 86
- stan-dev/rstan (sdk) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:18.489327+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:18:43.544036+00:00, confidence not recorded.
  - readme: https://github.com/stan-dev/stan (fetched 2026-08-28T04:07:18.489327+00:00, sha b801044ae27c)
  - homepage: https://mc-stan.org (fetched 2026-08-29T09:56:31.323185+00:00, sha 33b57b308ead)
  - site_page: https://mc-stan.org/about/index.html (fetched 2026-08-29T09:56:31.336658+00:00, sha d053f2da70df)
  - site_page: https://mc-stan.org/install (fetched 2026-08-29T09:56:31.338654+00:00, sha 8a7aa0503c45)
  - site_page: https://mc-stan.org/docs (fetched 2026-08-29T09:56:31.334886+00:00, sha 7c4223856da8)
  - site_page: https://mc-stan.org/install/index.html (fetched 2026-08-29T09:56:31.332583+00:00, sha c79971f04c71)
- Data as of 2026-08-30T08:39:29.467469+00:00.