BayesPostEst: Generate Postestimation Quantities for Bayesian MCMC Estimation

An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'.

Version:	0.1.0
Depends:	R (≥ 2.14.0)
Imports:	carData, caTools, coda (≥ 0.13), dplyr (≥ 0.5.0), ggmcmc, ggplot2, ggridges, R2jags, reshape2, rlang, stats, texreg, runjags, tidyr (≥ 0.5.1)
Suggests:	datasets, knitr, MCMCpack, rjags, rmarkdown, rstan (≥ 2.10.1), rstanarm, testthat, covr
Published:	2019-10-06
Author:	Johannes Karreth [aut], Shana Scogin [aut, cre], Rob Williams [aut], Andreas Beger [aut], Myunghee Lee [ctb], Neil Williams [ctb]
Maintainer:	Shana Scogin <shanarscogin at gmail.com>
BugReports:	https://github.com/ShanaScogin/BayesPostEst/issues
License:	GPL-3
URL:	https://github.com/ShanaScogin/BayesPostEst
NeedsCompilation:	no
SystemRequirements:	JAGS (http://mcmc-jags.sourceforge.net)
Materials:	README NEWS
CRAN checks:	BayesPostEst results

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