robust

Creates an object representing the robust prior of Bayarri
  et al (2012) that is  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
	     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.

robust: Robust-Prior Distribution for Coefficients in BMA Model

Description

Usage

Arguments

Value

Details

See Also

Examples