Package: BayesPostEst 0.3.2
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:
BayesPostEst_0.3.2.tar.gz
BayesPostEst_0.3.2.zip(r-4.5)BayesPostEst_0.3.2.zip(r-4.4)BayesPostEst_0.3.2.zip(r-4.3)
BayesPostEst_0.3.2.tgz(r-4.4-any)BayesPostEst_0.3.2.tgz(r-4.3-any)
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://shanascogin.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/shanascogin/bayespostest/issues
Last updated 2 years agofrom:149180df1a. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 04 2024 |
| R-4.5-win | NOTE | Sep 04 2024 |
| R-4.5-linux | NOTE | Sep 04 2024 |
| R-4.4-win | NOTE | Sep 04 2024 |
| R-4.4-mac | NOTE | Sep 04 2024 |
| R-4.3-win | NOTE | Sep 04 2024 |
| R-4.3-mac | NOTE | Sep 04 2024 |
Exports:mcmcAveProbmcmcCoefPlotmcmcFDmcmcFDplotmcmcMargEffmcmcObsProbmcmcRegmcmcRocPrcmcmcRocPrcGenmcmcTab
Dependencies:abindaskpassbitopsbootcarDatacaToolsclicodacolorspacecpp11curldplyrfansifarvergenericsggplot2ggridgesgluegplotsgtablegtoolsHDIntervalhttrisobandjsonliteKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvmimemunsellnlmeopensslpillarpkgconfigplyrpurrrR2jagsR2WinBUGSR6RColorBrewerRcppreshape2rjagsrlangROCRscalesstringistringrsystexregtibbletidyrtidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| BayesPostEst Overview | BayesPostEst |
| 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 |
