models and for large p truncated priors on the model
space.  The user may force variables to always be included.

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
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) <http://dx.doi.org/10.1198/jcgs.2010.09049>.

Suggests: MASS, knitr, rmarkdown, GGally

mixture of g-priors from Liang et al (2008)
for linear models or mixtures of g-priors in GLMs of Li and Clyde (2015)
samples models using the BAS tree structure as an efficient hash table.
Uniform priors over all models or beta-binomial prior distributions on
Clyde, Ghosh and Littman (2010)   <http://dx.doi.org/10.1198/jcgs.2010.09049>.

Clyde, Ghosh and Littman (2010)   <DOI:10.1198/jcgs.2010.09049>. The
DOI  for the stable package release is <DOI:10.5281/zenodo.154457>.
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

Imports: stats, graphics, utils, grDevices
Suggests: MASS, knitr, GGally, rmarkdown, roxygen2

Title: Bayesian Variable Selection and Model Averaging using Bayesian
Description: Package for Bayesian Variable Selection and  Model Averaging in linear models and

Author: Merlise Clyde [aut, cre, cph] (<https://orcid.org/-5469>),

Suggests: MASS, knitr, GGally, rmarkdown, roxygen2, dplyr

Suggests: MASS, knitr, GGally, rmarkdown, roxygen2, dplyr, glmbb,
for linear models or mixtures of g-priors in GLMs of Li and Clyde (2018)

person("Don", "van de Bergh", role="ctb"))
Suggests: MASS, knitr, ggplot2, GGally, rmarkdown, roxygen2, dplyr,
for linear models or mixtures of g-priors from  Li and Clyde
Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g.
using Sampling w/out Replacement or an efficient MCMC algorithm which
space may be used to enforce sampling models that are full rank.
The user may force variables to always be included in addition to imposing constraints
that higher order interactions are included only if their parents are
Author: Merlise Clyde [aut, cre, cph] (ORCID=0000-0002-3595-1872),