Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Stefan Zeugner, Martin Feldkircher
Title: Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R
Abstract: This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the "binomial-beta" prior on the model space and the so-called "hyper-g" specifications for Zellner's g prior. Furthermore, the BMS package allows the user to specify her own model priors and offers a possibility of subjective inference by setting "prior inclusion probabilities" according to the researcher's beliefs. Furthermore, graphical analysis of results is provided by numerous built-in plot functions of posterior densities, predictive densities and graphical illustrations to compare results under different prior settings. Finally, the package provides full enumeration of the model space for small scale problems as well as two efficient MCMC (Markov chain Monte Carlo) samplers that sort through the model space when the number of potential covariates is large.

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Paper: Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R     Download PDF (Downloads: 3912)
BMS_0.3.4.tar.gz: R source package Download (Downloads: 164; 487KB)
v68i04.R: R replication code Download (Downloads: 232; 3KB)

DOI: 10.18637/jss.v068.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.