Bayesian Model Averaging using Bayesian Adaptive Sampling
Description
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.