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Getting started with CmdStanR Jonah Gabry, Rok Češnovar, and Andrew Johnson Source: vignettes/cmdstanr.Rmd Introduction CmdStanR (Command Stan R) is a lightweight interface to Stan for R users that provides an alternative to the traditional RStan interface. See the Comparison with RStan section later in this vignette for more details on how the two interfaces differ. Using CmdStanR requires instal

This is the official user’s guide for Stan. It provides example models and programming techniques for coding statistical models in Stan. Part 1 gives Stan code and discussions for several important classes of models. Part 2 discusses various general Stan programming techniques that are not tied to any particular model. Part 3 introduces algorithms for calibration and model checking that require mu

StanCon 2026 has been announced! Join us August 17-21 in Uppsala, Sweden. Learn more. StanCon 2026 Uppsala More information available at stancon2026.org StanCon 2024 Oxford Location & Date: Oxford University, England – September 9-13, 2024 Program Recorded Talks: YouTube playlist Keynotes: The Pragmatic Probabilistic Programmer – Mitzi Morris Applied modeling for drug development – Sebastian Weber

Plot of the number of lynx and hare pelts (in thousands) collected by the Hudson’s Bay Company between 1900 and 1920. Plot of the number of pelts collected for lynx versus hares from 1900 to 1920. This plot is similar to that of the dynamics of a spring in phase space (i.e., position vs. momentum). The plot makes it clear that the spikes in the lynx population lag those in the hare population. Whe

Spatial Models in Stan: Intrinsic Auto-Regressive Models for Areal Data Mitzi Morris Update, August 2019 An expanded version of this case study is now available as: Bayesian Hierarchical Spatial Models: Implementing the Besag York Mollié Model in Stan Many thanks to my awesome co-authors: Katherine Wheeler-Martin Dan Simpson Stephen J. Mooney Andrew Gelman Charles DiMaggio When areal data has a sp

StanCon 2026 has been announced! Join us August 17-21 in Uppsala, Sweden. Learn more. Bayesian Modeling Stan enables sophisticated statistical modeling using Bayesian inference, allowing for more accurate and interpretable results in complex data scenarios. Flexible and Scalable Stan’s probabilistic programming language is suitable for a wide range of applications, from simple linear regression to