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BoomSpikeSlab
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]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.BoomSpikeSlab . BoomSpikeSlab results . BoomSpikeSlab.pdf . BoomSpikeSlab_1.2.6.tar.gz . BoomSpikeSlab_1.2.6.zip . BoomSpikeSlab_1.2.6.zip . BoomSpikeSlab_1.2.6.zip . BoomSpikeSlab_1.2.6.tgz . BoomSpikeSlab_1.2.6.tgz . BoomSpikeSlab_1.2.6.tgz . BoomSpikeSlab_1.2.6.tgz . BoomSpikeSlab archive . https://CRAN.R-project.org/package=BoomSpikeSlab .
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 04 May. 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)
Other packages that cited BoomSpikeSlab R package
View BoomSpikeSlab citation profile
Other R packages that BoomSpikeSlab depends,
imports, suggests or enhances
Complete documentation for BoomSpikeSlab
Functions, R codes and Examples using
the BoomSpikeSlab R package
Some associated functions: independent.spike.slab.prior . independent.student.spike.slab.prior . lm.spike . logit.spike . logit.zellner.prior . mlm.spike . mlm.spike.slab.prior . model.matrix.glm.spike . model.matrix . nested.regression . nnet . partial.dependence.plot . plot.BayesNnet . plot.coefficients . plot.lm.spike.fit . plot.lm.spike . plot.lm.spike.residuals . plot.logit.spike.fit.summary . plot.logit.spike . plot.logit.spike.residuals . plot.marginal.inclusion.probabilities . plot.model.size . plot.poisson.spike . plot.qreg.spike . poisson.spike . poisson.zellner.prior . predict.lm.spike . print.summary.lm.spike . probit.spike . qreg.spike . residuals.lm.spike . shrinkage.regression . spike.slab.glm.prior . spike.slab.prior.base . spike.slab.prior . splines . student.spike.slab.prior . suggest.burn . summarize_spike_slab_coefficients . summary.lm.spike . summary.logit.spike .
Some associated R codes: lm.spike.R . logit.spike.R . mlm.spike.R . model.matrix.R . nested.regression.R . nnet.R . partial-dependence.R . poisson.spike.R . probit.spike.R . qreg.spike.R . shrinkage.regression.R . spike.slab.prior.R . splines.R . Full BoomSpikeSlab package functions and examples
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