LearnBayes: Functions for Learning Bayesian Inference

LearnBayes contains 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
Published: 2014-05-29
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
CRAN checks: LearnBayes results

Downloads:

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.tar.gz
Windows binaries: r-devel: LearnBayes_2.15.zip, r-release: LearnBayes_2.15.zip, r-oldrel: LearnBayes_2.15.zip
OS X Snow Leopard binaries: r-release: LearnBayes_2.15.tgz, r-oldrel: LearnBayes_2.15.tgz
OS X Mavericks binaries: r-release: LearnBayes_2.15.tgz
Old sources: LearnBayes archive

Reverse dependencies:

Reverse depends: ARAMIS, psbcGroup
Reverse imports: cancerTiming, chemosensors, spdep
Reverse suggests: mistat