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BoomSpikeSlab  

MCMC for Spike and Slab Regression
View on CRAN: Click here


Download and install BoomSpikeSlab package within the R console
Install from CRAN:
install.packages("BoomSpikeSlab")

Install from Github:
library("remotes")
install_github("cran/BoomSpikeSlab")

Install by package version:
library("remotes")
install_version("BoomSpikeSlab", "1.2.6")



Attach the package and use:
library("BoomSpikeSlab")
Maintained by
Steven L. Scott
[Scholar Profile | Author Map]
First Published: 2014-06-24
Latest Update: 2023-12-17
Description:
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.
How to cite:
Steven L. Scott (2014). BoomSpikeSlab: MCMC for Spike and Slab Regression. R package version 1.2.6, https://cran.r-project.org/web/packages/BoomSpikeSlab. Accessed 03 Apr. 2025.
Previous versions and publish date:
0.4.1 (2014-06-24 10:54), 0.5.1 (2014-12-03 09:51), 0.5.2 (2014-12-04 08:23), 0.5.3 (2016-03-15 16:10), 0.6.0 (2016-05-21 15:42), 0.7.0 (2016-08-18 09:48), 0.8.0 (2017-04-08 22:31), 0.9.0 (2017-05-28 10:30), 1.0.0 (2018-04-30 14:43), 1.1.0 (2019-05-18 09:30), 1.1.1 (2019-06-07 17:20), 1.2.1 (2019-09-04 07:10), 1.2.2 (2020-03-31 18:50), 1.2.3 (2020-05-01 08:50), 1.2.4 (2021-04-06 09:20), 1.2.5 (2022-05-26 19:40)
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Complete documentation for BoomSpikeSlab
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