stan-fundamentals

Stan models require blocks in exact order: functions, data, transformed data, parameters, transformed parameters, model, and generated quantities. Empty blocks are allowed, providing flexible structure for probabilistic programming.

Stan supports scalar, vector, and matrix types alongside common priors and likelihoods. These type system features enable precise statistical modeling and Bayesian inference for complex probabilistic programs.

Use cmdstanr in R to compile and sample Stan models. This R integration streamlines the Bayesian inference workflow by connecting the Stan probabilistic programming language directly to your data analysis environment.

Best practices for Stan include vectorization, non-centered parameterization, and target syntax. These workflow patterns optimize probabilistic programming performance and improve model diagnostics for Bayesian inference.

Stan works with R through the cmdstanr package for Bayesian inference. This integration enables researchers to compile, sample, and diagnose statistical models directly within their R data analysis workflow.

Model diagnostics are essential when writing Stan programs to validate Bayesian inference results. They help audit statistical modeling outputs by identifying convergence issues and verifying probabilistic program correctness.