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).

Version: 0.2.2
Depends: R (≥ 3.0), stats, methods, utils
Imports: insight (≥ 0.3.0)
Suggests: BayesFactor, bridgesampling, brms, broom, covr, dplyr, emmeans, tidyr, GGally, ggplot2, ggridges, KernSmooth, knitr, lme4, logspline, see, rmarkdown, rstan, rstanarm, stringr, testthat
Published: 2019-06-20
Author: Dominique Makowski ORCID iD [aut, cre], Daniel Lüdecke ORCID iD [aut], Mattan S. Ben-Shachar ORCID iD [aut], Michael D. Wilson ORCID iD [aut]
Maintainer: Dominique Makowski <dom.makowski at>
License: GPL-3
NeedsCompilation: no
Language: en-GB
Citation: bayestestR citation info
Materials: README NEWS
CRAN checks: bayestestR results


Reference manual: bayestestR.pdf
Vignettes: Bayes Factors
Get Started with Bayesian Analysis
Credible Intervals (CI)
Example 1: Initiation to Bayesian models
Example 2: Confirmation of Bayesian skills
Example 3: Become a Bayesian master
Reporting Guidelines
In-Depth 1: Comparison of Point-Estimates
In-Depth 2: Comparison of Indices of Effect Existence
Probability of Direction (pd)
Region of Practical Equivalence (ROPE)
Package source: bayestestR_0.2.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: bayestestR_0.2.2.tgz, r-oldrel: bayestestR_0.2.0.tgz
Old sources: bayestestR archive

Reverse dependencies:

Reverse imports: performance, see, sjPlot, sjstats


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