spikeslab: Prediction and Variable Selection Using Spike and Slab Regression

Weighted generalized ridge regression offers unique advantages in correlated high dimensional problems. Such estimators can be efficiently computed using Bayesian spike and slab models and are effective for prediction. For sparse variable selection, a generalization of the elastic net can be used in tandem with these Bayesian estimates. In this article, we de scribe the R-software package spikeslab for implementing this new spike and slab prediction and variable selection methodology.