Bayesian Variable Selection with Hierarchical Priors

Bayesian variable selection for linear regression models using hierarchical priors. There is a prior that combines information across responses and one that combines information across covariates, as well as a standard spike and slab prior for comparison. An MCMC samples from the marginal posterior distribution for the 0-1 variables indicating if each covariate belongs to the model for each response.


Reference manual

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1.1-4 by Laurel Stell, 6 years ago

Browse source code at

Authors: Laurel Stell and Chiara Sabatti

Documentation:   PDF Manual  

GPL (>= 2) license

Imports coda, plyr, reshape2

Suggests foreach, doRNG

See at CRAN