bayesGDS (version 0.6.2)

Scalable Rejection Sampling for Bayesian Hierarchical Models

Description

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.

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Version

Install

install.packages('bayesGDS')

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Version

0.6.2

License

MPL (== 2.0)

Maintainer

Michael Braun

Last Published

March 16th, 2016

Functions in bayesGDS (0.6.2)

binary

Binary choice example

get.LML

Log marginal likelihood of model

sample.GDS

Collect draws from the target posterior distribution

get.cutoffs

Draw thresholds for the accept-reject stage of the BD sampling algorithm.

Deprecated

Deprecated functions

binary-data

Sample simulated data for binary choice model in vignette

bayesGDS-package

Braun and Damien Algorithm for Scalable Rejection Sampling