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This paper introduces the R package BayesVarSel which implements objectiveBayesian methodology for hypothesis testing and variable selection in linearmodels. The package computes posterior probabilities of the competinghypotheses/models and provides a suite of tools, specifically proposed in theliterature, to properly summarize the results. Additionally, \ourpack\ is armedwith functions to compute several types of model averaging estimations andpredictions with weights given by the posterior probabilities. BayesVarSelcontains exact algorithms to perform fast computations in problems of small tomoderate size and heuristic sampling methods to solve large problems. Thesoftware is intended to appeal to a broad spectrum of users, so the interfacehas been carefully designed to be highly intuititive and is inspired by thewell-known lm function. The issue of prior inputs is carefully addressed. Inthe default usage (fully automatic for the user)BayesVarSel implements thecriteria-based priors proposed by Bayarri et al (2012), but the advanced userhas the possibility of using several other popular priors in the literature.The package is available through the Comprehensive R Archive Network, CRAN. Weillustrate the use of BayesVarSel with several data examples.

BayesVarSel: Bayesian Testing, Variable Selection and model averaging in Linear Models using R