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 |
| 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: LearnBayes_2.15.1.tgz, r-oldrel: LearnBayes_2.15.1.tgz |
| Old sources: | LearnBayes archive |
| Reverse depends: | bayeslongitudinal, ProbBayes, psbcGroup |
| Reverse imports: | cancerTiming, evidence, RSSampling, spatialreg, spdep, weibulltools |
| Reverse suggests: | mistat |
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