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: 0.3.2 Depends: R (≥ 3.5.0) Imports: carData, caTools, coda (≥ 0.13), dplyr (≥ 0.5.0), ggplot2, ggridges, reshape2, rlang, stats, texreg, tidyr (≥ 0.5.1), HDInterval, ROCR, graphics, grDevices, R2jags, runjags, rstanarm, rjags, MCMCpack, R2WinBUGS, brms Suggests: datasets, knitr, rmarkdown, rstan (≥ 2.10.1), testthat, covr Published: 2021-11-11 DOI: 10.32614/CRAN.package.BayesPostEst 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.io) Materials: README NEWS CRAN checks: BayesPostEst results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=BayesPostEst to link to this page.
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