The R Journal: article published in 2010, volume 2:2

spikeslab: Prediction and Variable Selection Using Spike and Slab Regression
Hemant Ishwaran, Udaya B. Kogalur and J. Sunil Rao , The R Journal (2010) 2:2, pages 68-73.

Abstract 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 describe the R-software package spikeslab for implementing this new spike and slab prediction and variable selection methodology.

CRAN packages: lars, snow
CRAN Task Views implied by cited CRAN packages: HighPerformanceComputing, MachineLearning

@article{RJ-2010-018,
  author = {Hemant Ishwaran and Udaya B. Kogalur and J. Sunil Rao},
  title = {{spikeslab: Prediction and Variable Selection Using Spike and
          Slab Regression}},
  year = {2010},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2010-018},
  url = {https://doi.org/10.32614/RJ-2010-018},
  pages = {68--73},
  volume = {2},
  number = {2}
}