Bayesian Bayesian Inference Jong Hee Park 2019-01-29

Applied researchers interested in Bayesian statistics are increasingly attracted to R because of the ease of which one can code algorithms to sample from posterior distributions as well as the significant number of packages contributed to the Comprehensive R Archive Network (CRAN) that provide tools for Bayesian inference. This task view catalogs these tools. In this task view, we divide those packages into four groups based on the scope and focus of the packages. We first review R packages that provide Bayesian estimation tools for a wide range of models. We then discuss packages that address specific Bayesian models or specialized methods in Bayesian statistics. This is followed by a description of packages used for post-estimation analysis. Finally, we review packages that link R to other Bayesian sampling engines such as JAGS, OpenBUGS, WinBUGS, and Stan.

Bayesian packages for general model fitting

Bayesian packages for specific models or methods

Post-estimation tools

Packages for learning Bayesian statistics

Packages that link R to other sampling engines

The Bayesian Inference Task View is written by Jong Hee Park (Seoul National University, South Korea), Andrew D. Martin (University of Michigan, Ann Arbor, MI, USA), and Kevin M. Quinn (UC Berkeley, Berkeley, CA, USA). Please email the task view maintainer with suggestions.

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