BayesVarSel: Bayesian Variable selection in Linear Models

Within the context of the linear regression model, this package provides tools for the analysis of the variable selection problem from a Bayesian perspective. The default implementation takes advantage of a closed-form expression for the posterior probabilities that the prior proposed in Bayarri, Berger, Forte and Garcia-Donato (2012) produces. Alternatively, other priors, like Zellner (1986) g-prior, Zellner-Siow (1980,1984) or Liang, Paulo, Molina, Clyde and Berger (2008) can be used. BayesVarSel allows the calculations to be performed either exactly (sequential or parallel computation) or heuristically, using a Gibbs sampling algorithm studied in Garcia-Donato and Martinez-Beneito (2013).

Version: 1.5.1
Depends: R (≥ 2.15.0), parallel, MASS
Published: 2014-03-25
Author: Gonzalo Garcia-Donato and Anabel Forte
Maintainer: Anabel Forte <forte at>
License: GPL-2
NeedsCompilation: yes
CRAN checks: BayesVarSel results


Reference manual: BayesVarSel.pdf
Package source: BayesVarSel_1.5.1.tar.gz
OS X binary: BayesVarSel_1.5.1.tgz
Windows binary:
Old sources: BayesVarSel archive