alexandercoulter/DHBayes: Bayesian Hierarchical Sampler for Discrete (Binomial, Negative-Binomial, Poisson) Data

This package implements maximum likelihood estimation (MLE), maximum a posteriori estimation (MAPE), empirical Bayesian fitting, and full hierarchical sampling algorithms for data models handling various discrete group-conditional distributions. Supported data types are binomially distributed data, with beta conjugate prior setups; negative-binomially distributed data, with beta conjugate prior setups; and Poisson distributed data, with gamma prior setups. Options are available for user-specified hyperparameter values, or fitting them through empirical Bayesian methods.

install.packages("remotes")
remotes::install_github("alexandercoulter/DHBayes")