BAS is an R Package for Bayesian Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling from posterior distributions. Prior distributions on coefficients are from Zellner’s g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) for linear models or mixtures of g-priors in GLMs of Li and Clyde (2015). Other model selection criteria are based on AIC, BIC, and Empirical Bayes. Sampling probabilities may be updated based on the sampled models using Sampling w/out Replacement or an MCMC algorithm samples models using the BAS tree structure as an efficient hash table. The package allows uniform or beta-binomial prior distributions on models and for large p truncated priors on the model space. The user may force variables to always be included.
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The DOI for the stable release is http://dx.doi.org/10.5281/zenodo.59497