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.
- Version2.15.1
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release03/18/2018
Documentation
- VignetteIntroduction to Bayes Factors
- VignetteLearning About a Binomial Proportion
- VignetteIntroduction to Bayes using Discrete Priors
- VignetteIntroduction to Markov Chain Monte Carlo
- VignetteIntroduction to Multilevel Modeling
- MaterialLearnBayes.pdf
- MaterialIntroduction to Bayes Factors
- MaterialLearning About a Binomial Proportion
- MaterialIntroduction to Bayes using Discrete Priors
- MaterialIntroduction to Markov Chain Monte Carlo
- MaterialIntroduction to Multilevel Modeling
- In ViewsBayesian
- In ViewsDistributions
- In ViewsSurvival
- In ViewsTeachingStatistics
Team
Jim Albert
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Dependencies
- Reverse Depends3 packages
- Reverse Imports4 packages