Package for Bayesian Model Averaging in 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 Liang et
al hyper-g priors (JASA 2008). Other model selection criterian
include AIC and BIC. Sampling probabilities may be updated
based on the sampled models.  Allows uniform or beta-binomial
prior distributions on models.

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)
<http://dx.doi.org/10.1198/016214507000001337>
for linear models or mixtures of
g-priors in GLMs of Li and Clyde (2015)
<http://arxiv.org/abs/1503.06913 >. 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.

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)
<http://dx.doi.org/10.1198/016214507000001337>
for linear models or mixtures of
g-priors in GLMs of Li and Clyde (2015)
<http://arxiv.org/abs/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) <http://dx.doi.org/10.1198/jcgs.2010.09049>.