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

Documentation:

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

Downloads:

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

Linking:

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