Abstract
BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation
Highlights
BayesPostEst is an R (R Core Team, 2019) package with convenience functions to generate and present quantities of interest after estimating Bayesian regression models fit using MCMC via JAGS (Plummer, 2017), Stan (Stan Development Team, 2019), MCMCpack (Martin, Quinn, & Park, 2011), or other MCMC samplers
BayesPostEst offers a further contribution by (1) providing convenient summaries of MCMC estimates as used by social scientists and (2) implementing methods for interpreting estimates in generalized linear models that are widely used in the social sciences
BayesPostEst implements the popular model fit diagnostic of examining receiver-operating characteristic and precision-recall curves for Bayesian models
Summary
BayesPostEst is an R (R Core Team, 2019) package with convenience functions to generate and present quantities of interest after estimating Bayesian regression models fit using MCMC via JAGS (Plummer, 2017), Stan (Stan Development Team, 2019), MCMCpack (Martin, Quinn, & Park, 2011), or other MCMC samplers. Quantities of interest include predicted probabilities and changes in probabilities in generalized linear models and analyses of model fit using ROC curves and precision-recall curves. The package contains two functions to create publication-ready tables summarizing model results with an assessment of substantively meaningful effect sizes. The package currently consists of seven functions:
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