hyper.g

a scalar > 0. The hyper.g(alpha) is equivalent
    to CCH(alpha -2, 2, 0). Liang     et al recommended values in the
    range 2 < alpha_h 

alpha

Creates an object representing the hyper-g mixture of g-priors on
  coefficients for BAS. 


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>.

Merlise Clyde

Bayesian Model Averaging using Bayesian Adaptive Sampling

hyper.g function

a scalar > 0. The hyper.g(alpha) is equivalent
    to CCH(alpha -2, 2, 0). Liang     et al recommended values in the
    range 2 < alpha_h <= 3

hyper.g: Hyper-g-Prior Distribution for Coefficients in BMA Models

Description

Usage

Arguments

Value

Details

See Also