rstan: R Interface for Bayesian Statistical Inference with Stan

User-facing R functions parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library. The Stan project develops a probabilistic programming language implementing full Bayesian inference via Markov Chain Monte Carlo, variational approximation, and maximum likelihood estimation, using automatic differentiation for gradient evaluation. The interface is authored by Jiqiang Guo and hosted on the paperswithcode platform.

Use Cases

• Implementing full Bayesian statistical inference based on Markov Chain Monte Carlo methods described in the summary.
• Performing approximate Bayesian inference based on the variational approximation capability mentioned.
• Conducting maximum likelihood estimation with optional penalization based on the optimization features described.
• Developing statistical models that leverage automatic differentiation for gradient calculation as highlighted in the description.

Strengths

• Provides a direct interface to the Stan library, a major probabilistic programming language.
• Supports three core inference methods: MCMC, variational inference, and optimization.
• Uses automatic differentiation, which is noted for speed and accuracy in gradient evaluation.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Software package providing an R interface to the Stan library.

Time Range

Temporal coverage is unknown.

Freshness

Last update date is unknown; freshness unverified.

Geography

Spatial coverage is unknown.