bayesian model averaging employing fixed and flexible priors: the bms package for r

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2015
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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.
Reference Key
zeugner2015journalbayesian Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Stefan Zeugner;Martin Feldkircher
Journal open geospatial data, software and standards
Year 2015
DOI
10.18637/jss.v068.i04
URL
https://www.jstatsoft.org/index.php/jss/article/view/699
Keywords
empirical bayes rstatistics bms

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