MCMC for Spike and Slab Regression

Spike and slab regression with a variety of residual error distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a few others. Spike and slab regression is Bayesian regression with prior distributions containing a point mass at zero. The posterior updates the amount of mass on this point, leading to a posterior distribution that is actually sparse, in the sense that if you sample from it many coefficients are actually zeros. Sampling from this posterior distribution is an elegant way to handle Bayesian variable selection and model averaging. See for an explanation of the Gaussian case.


Reference manual

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1.2.4 by Steven L. Scott, 10 months ago

Browse source code at

Authors: Steven L. Scott <[email protected]>

Documentation:   PDF Manual  

Task views: Bayesian Inference

LGPL-2.1 | file LICENSE license

Depends on Boom

Suggests MASS, testthat, mlbench, igraph

Linking to Boom

Depended on by bsts.

See at CRAN