braunm/bayesGDS: Scalable Rejection Sampling for Bayesian Hierarchical Models

Functions for implementing the Braun and Damien (2015) rejection sampling algorithm for Bayesian hierarchical models. The algorithm generates posterior samples in parallel, and is scalable when the individual units are conditionally independent.

Getting started

Package details

MaintainerMichael Braun <[email protected]>
LicenseMPL (== 2.0)
Version0.6.2
URL coxprofs.cox.smu.edu/braunm
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("braunm/bayesGDS")
braunm/bayesGDS documentation built on May 13, 2019, 2:31 a.m.