The R Journal: accepted article

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Bayesian Testing, Variable Selection and Model Averaging in Linear Models using R with BayesVarSel PDF download
Gonzalo Garcia-Donato and Anabel Forte

Abstract In this paper, objective Bayesian methods for hypothesis testing and variable selection in linear models are considered. The focus is on BayesVarSel, an R package that computes posterior probabilities of hypotheses/models and provides a suite of tools to properly summarize the results. We introduce the usage of specific functions to compute several types of model averaging estimations and predictions weighted by posterior probabilities. BayesVarSel contains exact algorithms to perform fast computations in problems of small to moderate size and heuristic sampling methods to solve large problems. We illustrate the functionalities of the package with several data examples.

Received: 2017-06-14; online 2018-05-21, supplementary material, (4.4 Kb)
CRAN packages: BayesVarSel, faraway, BayesFactor, BMS, mombf, BAS, BMA
CRAN Task Views implied by cited CRAN packages: Bayesian, Econometrics, SocialSciences, Survival


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-021,
  author = {Gonzalo Garcia-Donato and Anabel Forte},
  title = {{Bayesian Testing, Variable Selection and Model Averaging in
          Linear Models using R with BayesVarSel}},
  year = {2018},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-021/index.html}
}