Jonah Sol Gabry

Software and papers

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Stan

Stan is a statistical modeling language along with inference algorithms for full Bayesian inference, approximate Bayesian inference, and penalized maximum likelihood estimation. Stan is implemented in C++ but we provide interfaces for the command line, R and Python (and more).

R packages

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The rstan package is the R interface to Stan.
website | CRAN | source code on GitHub


RStanTools

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The rstantools R package provides tools for developing R packages interfacing with Stan.
website | CRAN | source code on GitHub


RStanARM

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The rstanarm package is for Bayesian applied regression modeling (ARM) via Stan. It is an appendage to the rstan package that enables some of the most common applied regression models to be estimated using Markov Chain Monte Carlo, variational approximations to the posterior distribution, or optimization. The rstanarm package allows these models to be specified using the customary R modeling syntax (e.g., like that of glm with a formula and a data.frame).
website | CRAN | source code on GitHub


ShinyStan CRAN_Status_Badge RStudio_CRAN_mirror_downloads_badge

The shinystan R package provides a graphical user interface providing interactive visual and numerical summaries of model parameters and convergence diagnostics for Bayesian models estimated using MCMC.
website | CRAN | source code on GitHub


BayesPlot

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The bayesplot R package provides a library of plotting functions for use after fitting Bayesian models (typically with MCMC).
website | CRAN | source code on GitHub

A paper about bayesplot and visualization in the Bayesian workflow more generally:


Loo

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The loo R package is for efficient approximate leave-one-out cross-validation for Bayesian models.
website | CRAN | source code on GitHub

The package implements the methods described in these papers with Aki and Andrew:

Papers

Preprints:

Tutorials and educational materials

A selection of tutorial vignettes on practical topics in Bayesian data analysis.

Visualization

Bayesian regression modeling with rstanarm

Predictive performance and model comparison