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 (to appear 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.

Merlise Clyde <clyde@stat.duke.edu> with contributions from
Michael Littman amd Joyee Ghosh
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.

c(person("Merlise", "Clyde", email="clyde@stat.duke.edu",
role=c("aut","cre", "cph")),
person("Michael", "Littman", role="ctb"),
person("Quanli", "Wang", role="ctb"),
person("Joyee", "Ghosh", role="ctb"),
person("Yingbo", "Li", role="ctb"))
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
Liang et al hyper-g priors (JASA 2008) 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 may force variables to always be included.
Merlise Clyde [aut, cre, cph],
Michael Littman [ctb],
Quanli Wang [ctb],
Joyee Ghosh [ctb],
Yingbo Li [ctb]