LearnBayes: Functions for Learning Bayesian Inference

A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

Version: 2.15.1
Published: 2018-03-18
DOI: 10.32614/CRAN.package.LearnBayes
Author: Jim Albert
Maintainer: Jim Albert <albert at bgsu.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: Bayesian, Distributions, Survival, TeachingStatistics
CRAN checks: LearnBayes results


Reference manual: LearnBayes.pdf
Vignettes: Introduction to Bayes Factors
Learning About a Binomial Proportion
Introduction to Bayes using Discrete Priors
Introduction to Markov Chain Monte Carlo
Introduction to Multilevel Modeling


Package source: LearnBayes_2.15.1.tar.gz
Windows binaries: r-devel: LearnBayes_2.15.1.zip, r-release: LearnBayes_2.15.1.zip, r-oldrel: LearnBayes_2.15.1.zip
macOS binaries: r-release (arm64): LearnBayes_2.15.1.tgz, r-oldrel (arm64): LearnBayes_2.15.1.tgz, r-release (x86_64): LearnBayes_2.15.1.tgz, r-oldrel (x86_64): LearnBayes_2.15.1.tgz
Old sources: LearnBayes archive

Reverse dependencies:

Reverse depends: bayeslongitudinal, ProbBayes, psbcGroup
Reverse imports: evidence, RSSampling, spatialreg, winputall


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