This package is considered a duplicate. The official version of this package is found at:https://shanascogin.r-universe.dev/BayesPostEst

Package: BayesPostEst 0.3.2

Shana Scogin

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'.

Authors:Johannes Karreth [aut], Shana Scogin [aut, cre], Rob Williams [aut], Andreas Beger [aut], Myunghee Lee [ctb], Neil Williams [ctb]

BayesPostEst_0.3.2.tar.gz
BayesPostEst_0.3.2.tar.gz(r-4.5-noble)BayesPostEst_0.3.2.tar.gz(r-4.4-noble)
BayesPostEst_0.3.2.tgz(r-4.4-emscripten)BayesPostEst_0.3.2.tgz(r-4.3-emscripten)
BayesPostEst.pdf |BayesPostEst.html✨
BayesPostEst/json (API)
NEWS

# Install 'BayesPostEst' in R:

install.packages('BayesPostEst', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/shanascogin/bayespostest/issues

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC
c++– GNU Standard C++ Library v3

Datasets:

jags_interactive - Fitted JAGS interactive linear model
jags_interactive_cat - Fitted JAGS interactive linear model with categorical moderator
jags_logit - Fitted JAGS logit model
jags_probit - Fitted JAGS probit model
sim_data - Simulated data for examples
sim_data_interactive - Simulated data for examples
sim_data_interactive_cat - Simulated data for examples

jags cpp

2.93 score 17 scripts 275 downloads 1 mentions 10 exports 149 dependencies

Last updated 3 years agofrom:8efc5a7c29. Checks:OK: 1 NOTE: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 18 2024
R-4.5-linux	NOTE	Dec 18 2024

Exports:mcmcAveProb mcmcCoefPlot mcmcFD mcmcFDplot mcmcMargEff mcmcObsProb mcmcReg mcmcRocPrc mcmcRocPrcGen mcmcTab

Dependencies:abind askpass backports base64enc bayesplot BH bitops boot bridgesampling brms Brobdingnag bslib cachem callr carData caTools checkmate cli coda codetools colorspace colourpicker commonmark cpp11 crayon crosstalk curl desc digest distributional dplyr DT dygraphs evaluate fansi farver fastmap fontawesome fs future future.apply generics ggplot2 ggridges globals glue gplots gridExtra gtable gtools HDInterval highr htmltools htmlwidgets httpuv httr igraph inline isoband jquerylib jsonlite KernSmooth knitr labeling later lattice lazyeval lifecycle listenv lme4 loo magrittr markdown MASS Matrix MatrixModels matrixStats mcmc MCMCpack memoise mgcv mime miniUI minqa munsell mvtnorm nleqslv nlme nloptr numDeriv openssl parallelly pillar pkgbuild pkgconfig plyr posterior processx promises ps purrr quantreg QuickJSR R2jags R2WinBUGS R6 rappdirs RColorBrewer Rcpp RcppEigen RcppParallel reshape2 rjags rlang rmarkdown ROCR rstan rstanarm rstantools runjags sass scales shiny shinyjs shinystan shinythemes sourcetools SparseM StanHeaders stringi stringr survival sys tensorA texreg threejs tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun xtable xts yaml zoo

Using the BayesPostEst package

Johannes Karreth, Shana Scogin, Rob Williams, Andreas Beger

Rendered fromgetting_started.Rmdusingknitr::rmarkdownon Dec 18 2024.

Last update: 2021-11-11
Started: 2019-08-05

Help page	Topics
BayesPostEst Overview	BayesPostEst
Fitted JAGS interactive linear model	jags_interactive
Fitted JAGS interactive linear model with categorical moderator	jags_interactive_cat
Fitted JAGS logit model	jags_logit
Fitted JAGS probit model	jags_probit
Predicted Probabilities using Bayesian MCMC estimates for the "Average" Case	mcmcAveProb
Coefficient Plots for MCMC Output	mcmcCoefPlot
First Differences of a Bayesian Logit or Probit model	mcmcFD
Marginal Effects Plots for MCMC Output	mcmcMargEff
Predicted Probabilities using Bayesian MCMC estimates for the Average of Observed Cases	mcmcObsProb
LaTeX or HTML regression tables for MCMC Output	mcmcReg
ROC and Precision-Recall Curves using Bayesian MCMC estimates generalized	mcmcRocPrcGen
Summarize Bayesian MCMC Output R function for summarizing MCMC output in a regression-style table.	mcmcTab
Plot Method for First Differences from MCMC output	plot.mcmcFD
ROC and Precision-Recall Curves using Bayesian MCMC estimates	as.data.frame.mcmcRocPrc mcmcRocPrc mcmcRocPrc.brmsfit mcmcRocPrc.bugs mcmcRocPrc.default mcmcRocPrc.jags mcmcRocPrc.mcmc mcmcRocPrc.rjags mcmcRocPrc.runjags mcmcRocPrc.stanfit mcmcRocPrc.stanreg plot.mcmcRocPrc print.mcmcRocPrc
Simulated data for examples	sim_data
Simulated data for examples	sim_data_interactive
Simulated data for examples	sim_data_interactive_cat

Package: BayesPostEst 0.3.2

BayesPostEst: Generate Postestimation Quantities for Bayesian MCMC Estimation

Using the BayesPostEst package

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)