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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("BoomSpikeSlab")

1.2.3 by Steven L. Scott, 6 months ago


Browse source code at https://github.com/cran/BoomSpikeSlab


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