Packaged: 2021-09-25 02:24:11 UTC; minedogucu

Depends: stats, MASS, mnormt, gplots, combinat
Author: Christian P. Robert, Universite Paris Dauphine, and Jean-Michel
Maintainer: Christian P. Robert <xian@ceremade.dauphine.fr>
Description: bayess contains a collection of functions that allows the
reenactment of the R programs used in the book "Bayesian
Essentials with R" (revision of "Bayesian Core") without
further programming. R code being available as well, they can
be modified by the user to conduct one's own simulations.

Depends: stats, mnormt, gplots, combinat
Description: Allows the reenactment of the R programs used in
the book Bayesian Essentials with R without further programming.
R code being available as well, they can be modified by the user

Description: Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.

Title: Effect Size Targeted Bayesian Two-Sample t-Tests via Markov
Chain Monte Carlo in Gaussian Mixture Models

Title: Understand and Describe Bayesian Models and Posterior
Description: 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).
Suggests: brms, broom, covr, dplyr, tidyr, ggplot2, ggridges, knitr,
Author: Dominique Makowski [aut, cre] (<https://orcid.org/0000-0001-5375-9967>),

comment = c(ORCID = "0000-0002-8895-3206", Twitter = "@strengejacke")),
comment = c(ORCID = "0000-0003-1995-6531", Twitter = "@patilindrajeets")),
Description: Provides utilities to describe posterior
distributions and Bayesian models. It includes point-estimates such as
Maximum A Posteriori (MAP), measures of dispersion (Highest Density
indices used for null-hypothesis testing (such as ROPE percentage, pd
Imports: insight (>= 0.13.2), methods, stats, utils
Suggests: BayesFactor, bayesQR, blavaan, bridgesampling, brms, dplyr,
effectsize, emmeans, GGally, ggplot2, ggridges, httr,
KernSmooth, knitr, lavaan, lme4, logspline, MASS, mclust,
mediation, modelbased, parameters, performance, rmarkdown,
rstan, rstanarm, see, spelling, stringr, testthat, tidyr,
Author: Dominique Makowski [aut, cre] (<https://orcid.org/0000-0001-5375-9967>,
Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>,