BayesSummaryStatLM: MCMC Sampling of Bayesian Linear Models via Summary Statistics

Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. Summary statistics are useful for systems with very limited amounts of physical memory. The package provides two functions: one function that computes summary statistics of data and one function that carries out the MCMC posterior sampling for Bayesian linear regression models where summary statistics are used as input. The function read.regress.data.ff utilizes the R package 'ff' to handle data sets that are too large to fit into a user's physical memory, by reading in data in chunks.

Version: 1.0-1
Depends: R (≥ 3.1.1), mvnfast, ff
Published: 2015-03-03
Author: Evgeny Savel'ev, Alexey Miroshnikov, Erin Conlon
Maintainer: Evgeny Savel'ev <savelev at vt.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: Bayesian
CRAN checks: BayesSummaryStatLM results

Downloads:

Reference manual: BayesSummaryStatLM.pdf
Package source: BayesSummaryStatLM_1.0-1.tar.gz
Windows binaries: r-devel: BayesSummaryStatLM_1.0-1.zip, r-release: BayesSummaryStatLM_1.0-1.zip, r-oldrel: BayesSummaryStatLM_1.0-1.zip
OS X Mavericks binaries: r-release: BayesSummaryStatLM_1.0-1.tgz, r-oldrel: BayesSummaryStatLM_1.0-1.tgz
Old sources: BayesSummaryStatLM archive

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