# Stan User's Guide The Stan user's guide provides example models and programming techniques for coding statistical models in Stan. It also serves as an example-driven introduction to Bayesian modeling and inference. For versions 2.18 and later, this is titled _Stan User's Guide_. For versions 2.17 and earlier, this is part of the _Stan Reference Manual_. * [Stan User's Guide 2.30](/docs/stan-users-guide/index.html)     (html) * [Stan User's Guide 2.30 pdf](/docs/2_30/stan-users-guide-2_30.pdf)     (GitHub pdf,  CC-BY 4.0 license) # Stan Language Reference Manual The manual for Stan's programming language for coding probability models, inference algorithms for fitting models and making predictions, and posterior analysis tools for evaluating the results. This manual applies to all Stan interfaces. * [Stan Language Reference Manual 2.30](/docs/reference-manual/index.html)     (html) * [Stan Language Reference Manual 2.30 pdf](/docs/2_30/reference-manual-2_30.pdf)     (GitHub pdf,  CC-BY 4.0 license) # Stan Language Functions Reference The reference for the functions defined in the Stan math library and available in the Stan programming language. For versions 2.17 and earlier, this is part of the _Stan Reference Manual_. * [Stan Language Functions Reference 2.30](/docs/functions-reference/index.html)     (html) * [Stan Language Functions Reference 2.30 pdf](/docs/2_30/functions-reference-2_30.pdf)     (GitHub pdf,  CC-BY 4.0 license) # Stan Software Development Lifecycle The software development practices underlying the code managed by the Stan project is hosted at * [Stan Software Development Lifecycle](/docs/sdlc.html) # CmdStan Documentation * [CmdStan User's Guide 2.30](/docs/cmdstan-guide/index.html)     (html) * [CmdStan User's Guide 2.30 pdf](/docs/2_30/cmdstan-guide-2_30.pdf)     (GitHub pdf,  CC-BY 4.0 license) # CmdStanR, CmdStanPy Documentation CmdStanPy and CmdStanR provide access to the latest version of Stan, or earlier versions, as specified. They use minimal memory beyond what is used by CmdStan itself to run and record an analysis, therefore they can be used to fit more complex models and/or large datasets. * [CmdStanR Vignettes, tutorials, and other package information](/cmdstanr) * [CmdStanPy documentation](/cmdstanpy) # RStan Documentation * [RStan Vignettes, tutorials, and other package information](/rstan) # PyStan Documentation * [PyStan Documentation on]( # Case Studies and Notebooks Case studies provide longer, more-detailed discussion of various applications, models, and methodologies. Each case study is written in knitr or Jupyter notebooks so that the discussion is accompanied with working code. Case studies written or validated by the Stan development team can be found at *

Case Studies

The proceedings of each Stan Conference also take the form of self-contained notebooks. StanCon notebooks are hosted on GitHub along with other materials from the conferences: *

StanCon Materials     (GitHub, with video links)

# Tutorials The Stan development team and many users have contributed tutorials aimed at introducing users to various aspects of statistical modeling with Stan, both in written and visual formats. These tutorials can be found at *


# Specialized Field Guides Tutorials, case studies, software packages, and publications related to specific fields. These pages are maintained by volunteers from the Stan community. If you would like to contribute one for your field please reach out on the Stan forums. *

Stan for Education Research   (GitHub)


Stan for Ecology   (GitHub)


Stan for Epidemiology   (GitHub)


Stan for Cognitive Science   (GitHub)

# The Stan Forums The most up to date discussion of modeling techniques and computational issues if often found in the Stan Forums before it ends up in a case study or a paper. *

Stan Forums (Discourse)

Discussions prior to June 2017 are archived and public on the now deprecated Stan Users Google Group. # GitHub Stan Developer Wiki The Stan Wiki is largely focused on development documentation but it also includes a few pages with helpful information for users. *

Stan Wiki     (GitHub)

One particularly recommended page is *

Prior Choice Recommendations     (GitHub)

# Further References Finally, there are many works that elaborate on aspects of Stan from algorithms to applications. Some of the more relevant references can be found at *

External References