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, 2014 <doi:10.1016/B978-0-12-405888-0.09999-2>) and indices used for null-hypothesis testing (such as ROPE percentage and pd).

Version: 0.1.0
Depends: R (≥ 3.0), stats
Imports: insight
Suggests: brms, broom, covr, dplyr, tidyr, ggplot2, ggridges, knitr, rmarkdown, rstanarm, stringr, testthat
Published: 2019-04-08
Author: Dominique Makowski ORCID iD [aut, cre], Daniel Lüdecke ORCID iD [aut]
Maintainer: Dominique Makowski <dom.makowski at>
License: GPL-3
NeedsCompilation: no
Language: en-GB
Citation: bayestestR citation info
Materials: NEWS
CRAN checks: bayestestR results


Reference manual: bayestestR.pdf
Vignettes: Get Started with Bayesian Analysis
Example 1: Bayesian (General) Linear Models
Reporting Guidelines
In-Depth 1: Comparison of Point-Estimates
In-Depth 2: Comparison of Indices of Effect Existence
Package source: bayestestR_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: bayestestR_0.1.0.tgz, r-oldrel: bayestestR_0.1.0.tgz

Reverse dependencies:

Reverse imports: performance, sjPlot


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