BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling

Package for Bayesian Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement 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 include AIC and BIC. 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. 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.

Version: 1.2.1
Depends: R (≥ 3.0), stats, graphics
Suggests: MASS
Published: 2016-04-16
Author: Merlise Clyde [aut, cre, cph], Michael Littman [ctb], Quanli Wang [ctb], Joyee Ghosh [ctb], Yingbo Li [ctb]
Maintainer: Merlise Clyde <clyde at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: BAS citation info
Materials: ChangeLog
CRAN checks: BAS results


Reference manual: BAS.pdf
Package source: BAS_1.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: BAS_1.2.1.tgz, r-oldrel: BAS_1.2.1.tgz
Old sources: BAS archive