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sparsevb
View on CRAN: Click
here
Download and install sparsevb package within the R console
Install from CRAN:
install.packages("sparsevb")
Install from Github:
library("remotes")
install_github("cran/sparsevb") Install by package version:
library("remotes")
install_version("sparsevb", "0.1.1") Attach the package and use:
library("sparsevb")
Maintained by
Gabriel Clara
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First Published: 2021-01-15
Latest Update: 2025-01-24
Description:
Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <doi:10.48550/arXiv.2010.11665>.
How to cite:
Gabriel Clara (2021). sparsevb: Spike-and-Slab Variational Bayes for Linear and Logistic Regression. R package version 0.1.1, https://cran.r-project.org/web/packages/sparsevb. Accessed 15 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:07), 0.1.0 (2021-01-15 10:20)
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Complete documentation for sparsevb
Functions, R codes and Examples using
the sparsevb R package
Some associated functions: sparsevb-package . svb.fit .
Some associated R codes: RcppExports.R . sparsevb-package.R . svb.fit.R . Full sparsevb package functions and examples
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