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
Browse package contents
Package details |
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| Maintainer | Michael Braun <braunm@smu.edu> |
| License | MPL (== 2.0) |
| Version | 0.6.2 |
| URL | coxprofs.cox.smu.edu/braunm |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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