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RStan is the R interface to Stan, a C++ library for statistical modeling and high-performance statistical computation. It lets users specify models in the Stan modeling language (for Bayesian inference), compile them, and perform inference from R. Key inference approaches include full Bayesian inference via Hamiltonian Monte Carlo (specifically the No-U-Turn Sampler, NUTS), approximate Bayesian inference via variational methods, and optimization (penalized likelihood). RStan integrates with Stan’s automatic differentiation library, provides diagnostics, model comparison, posterior predictive checks, etc. It is used in research, applied statistics, and modelling workflows where flexibility and rigor in Bayesian methods are required.
Features
- Full Bayesian inference via NUTS (No-U-Turn Sampler) for flexible posterior sampling
- Automatic differentiation variational inference (ADVI) for faster approximate Bayesian inference
- Optimization for obtaining point estimates / penalized maximum likelihood using algorithms like L-BFGS
- Model specification in Stan language with support for hierarchical / multilevel models, custom probability functions, etc.
- Diagnostics and post-processing: posterior predictive checks, convergence diagnostics, examination of model fit etc.
- Integration in R environment: ability to compile models from R, manage data and outputs via R objects, work with StanHeaders, and use in interactive / script workflows
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