bayestestR (0.5.3)

Understand and Describe Bayesian Models and Posterior Distributions.

https://easystats.github.io/bayestestR/
http://cran.r-project.org/web/packages/bayestestR

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 ) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors).

Maintainer: Dominique Makowski
Author(s): Dominique Makowski [aut, cre] (<https://orcid.org/0000-0001-5375-9967>), Daniel Ldecke [aut] (<https://orcid.org/0000-0002-8895-3206>), Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>), Michael D. Wilson [aut] (<https://orcid.org/0000-0003-4143-7308>), Paul-Christian Brkner [rev], Tristan Mahr [rev] (<https://orcid.org/0000-0002-8890-5116>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Quentin F. Gronau [ctb] (<https://orcid.org/0000-0001-5510-6943>)

License: GPL-3

Uses: insight, KernSmooth, ggplot2, lme4, logspline, mclust, tweedie, MASS, stringr, testthat, GGally, knitr, BayesFactor, dplyr, tidyr, rmarkdown, broom, brms, covr, rstan, rstanarm, bridgesampling, ggridges, emmeans, performance, see, modelbased
Reverse suggests: coveffectsplot, insight, see

Released 11 days ago.