Package: BayesVarSel
Type: Package
Title: Bayesian Variable selection in Linear Models
Version: 1.1
Date: 2013-05-06
Author: Gonzalo Garcia-Donato and Anabel Forte
Maintainer: Anabel Forte <forte@uji.es>
Description: Within the context of the lineal model, this package
provides tools for the analysis of the variable selection
problem from a Bayesian perspective. Special emphasis is placed
on providing simple and intuitive methods to explore and
synthesize the results. 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 (2012). 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. Nevertheless, other alternative priors, like Zellner
(1986) g-prior, Zellner-Siow (1980,1984) or Liang, Paulo,
Molina, Clyde and Berger (2008) can be used.
Depends: R (>= 2.15.0), parallel, MASS
License: GPL-2
Packaged: 2013-05-07 14:48:35 UTC; root
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-05-07 17:22:16

Description: 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).

Title: Bayes Factors, Model Choice And Variable Selection In Linear
Description: Conceived to calculate Bayes factors in linear models and then to provide a formal Bayesian answer to testing and variable selection problems. From a theoretical side, the emphasis in the package is placed on the prior distributions and BayesVarSel allows using a wide range of them: Jeffreys-Zellner-Siow (Jeffreys, 1961; Zellner and Siow, 1980,1984) Zellner (1986); Fernandez et al. (2001), Liang et al. (2008) and Bayarri et al. (2012). The interaction with the package is through a friendly interface that syntactically mimics the well-known lm command of R. The resulting objects can be easily explored providing the user very valuable information (like marginal, joint and conditional inclusion probabilities of potential variables; the highest posterior probability model, HPM; the median probability model, MPM) about the structure of the true -data generating- model. Additionally, BayesVarSel incorporates abilities to handle problems with a large number of potential explanatory variables through parallel and heuristic versions (Garcia-Donato and Martinez-Beneito 2013) of the main commands.