Provides foundational knowledge for writing, reviewing, and optimizing high-performance Stan 2.37 Bayesian models.
The Stan Fundamentals skill equips Claude with the domain-specific syntax, program architecture, and statistical best practices required to build robust Bayesian models. It covers the essential seven-block program structure, the constrained type system, and critical optimization techniques like vectorization and non-centered parameterization. Whether you are translating models from BUGS/JAGS or building complex hierarchical models from scratch, this skill ensures your Stan code follows modern conventions and integrates seamlessly with workflows like cmdstanr and Statistical Rethinking.
Key Features
01Diagnostic checklists for MCMC convergence, Rhat, and Effective Sample Size
02Comprehensive reference for constrained types and modern array syntax
03Implementation templates for priors, distributions, and generated quantities
04Standardized 7-block Stan program structure guidance
050 GitHub stars
06Advanced optimization patterns including vectorization and non-centered parameterization
Use Cases
01Optimizing slow-running Stan models to improve sampling efficiency and reduce divergences
02Translating legacy probabilistic models from BUGS or JAGS into modern Stan syntax
03Building new Bayesian hierarchical models from scratch with proper constraints
