R package bayestestR: Understand and Describe Bayesian Models and Posterior Distributions. Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors).
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Daniel Ludecke; Mattan S. Ben-Shachar; Indrajeet Patil; Dominique Makowski: Extracting, Computing and Exploring the Parameters of Statistical Models using R (2020) not zbMATH
- Jhwueng, Dwueng-Chwuan: Modeling rate of adaptive trait evolution using Cox-Ingersoll-Ross process: an approximate Bayesian computation approach (2020)
- Riko Kelter: fbst: An R package for the Full Bayesian Significance Test for testing a sharp null hypothesis against its alternative via the e-value (2020) arXiv
- Daniel Lüdecke, Philip D. Waggoner, Dominique Makowski: insight: A Unified Interface to Access Information fromModel Objects in R (2019) not zbMATH
- Dominique Makowski, Mattan S. Ben-Shachar, Daniel Lüdecke: bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework (2019) not zbMATH
- Shana Scogin; Johannes Karreth; Andreas Beger; Rob Williams: BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation (2019) not zbMATH