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.


News

NEWS file for bayesGDS package

  • Patch. Changes in tests and vignettes to enable compatibility with sparseHessianFD 0.3.0.
  • Added devtools infrastructure.

  • Documentation now created using roxygen2

  • All new vignettes

  • New CITATION information

VERSION 0.6.0 (December 13, 2013) [first entry in NEWS file]

  • This is a complete reworking of the package, with new function names and arguments. The function for the rejection sampling phase is now sample.GDS. Function arguments have changed. Altogether, the sample.GDS phase should run much more efficiently than previous versions.

  • The dependency to Rmfpr has been removed.

  • An updated version of Braun and Damien (2013) is available in the /doc folder of the package.

Reference manual

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install.packages("bayesGDS")

0.6.2 by Michael Braun, 2 years ago


coxprofs.cox.smu.edu/braunm


Browse source code at https://github.com/cran/bayesGDS


Authors: Michael Braun [aut, cre, cph]


Documentation:   PDF Manual  


MPL (== 2.0) license


Depends on Matrix

Suggests sparseHessianFD, sparseMVN, mvtnorm, trustOptim, plyr, dplyr, testthat, knitr, R.rsp, MCMCpack


See at CRAN