BAS

Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

Package for Bayesian Variable Selection and 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) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors in GLMs of Li and Clyde (2015) <arXiv:1503.06913>. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using Sampling w/out Replacement or an efficient MCMC algorithm samples models using the BAS tree structure as an efficient hash table. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used. The user may force variables to always be included. Details behind the sampling algorithm are provided in Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049>. This material is based upon work supported by the National Science Foundation under Grant DMS-1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Description file content

Package
BAS
Version
1.5.1
Date
2018-05-15
Title
Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Depends
R (>= 3.0)
Imports
stats, graphics, utils, grDevices
Suggests
MASS, knitr, GGally, rmarkdown, roxygen2, dplyr
Description
Package for Bayesian Variable Selection and 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, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using Sampling w/out Replacement or an efficient MCMC algorithm samples models using the BAS tree structure as an efficient hash table. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used. The user may force variables to always be included. Details behind the sampling algorithm are provided in Clyde, Ghosh and Littman (2010) . This material is based upon work supported by the National Science Foundation under Grant DMS-1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
License
GPL (>= 2)
URL
BugReports
https://github.com/merliseclyde/BAS/issues
Repository
CRAN
NeedsCompilation
yes
ByteCompile
yes
VignetteBuilder
knitr
RoxygenNote
6.0.1
Packaged
2018-06-06 00:45:24 UTC; mclyde
Author
Merlise Clyde [aut, cre, cph] (), Michael Littman [ctb], Quanli Wang [ctb], Joyee Ghosh [ctb], Yingbo Li [ctb]
Maintainer
Merlise Clyde
Date/Publication
2018-06-07 13:51:08 UTC

install.packages('BAS')

1.5.1

11 days ago

https://www.r-project.org

Merlise Clyde

GPL (>= 2)

Depends on

R (>= 3.0)

Imports

stats, graphics, utils, grDevices

Suggests

MASS, knitr, GGally, rmarkdown, roxygen2, dplyr

Discussions