sparsevb: Spike-and-Slab Variational Bayes for Linear and Logistic Regression

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 (JASA 2020) and Kolyan Ray, Botond Szabo, and Gabriel Clara (NeurIPS 2020).

Version:	0.1.1
Imports:	Rcpp (≥ 1.0.5), selectiveInference (≥ 1.2.5), glmnet (≥ 4.0-2), stats
LinkingTo:	Rcpp, RcppArmadillo, RcppEnsmallen
Published:	2025-01-24
DOI:	10.32614/CRAN.package.sparsevb
Author:	Gabriel Clara [aut, cre], Botond Szabo [aut], Kolyan Ray [aut]
Maintainer:	Gabriel Clara <gabriel.j.clara at gmail.com>
BugReports:	https://gitlab.com/gclara/varpack/-/issues
License:	GPL (≥ 3)
NeedsCompilation:	yes
CRAN checks:	sparsevb results

Documentation:

Reference manual:

sparsevb.pdf

Downloads:

Package source:	sparsevb_0.1.1.tar.gz
Windows binaries:	r-devel: sparsevb_0.1.1.zip, r-release: sparsevb_0.1.1.zip, r-oldrel: sparsevb_0.1.1.zip
macOS binaries:	r-devel (arm64): sparsevb_0.1.1.tgz, r-release (arm64): sparsevb_0.1.1.tgz, r-oldrel (arm64): sparsevb_0.1.1.tgz, r-devel (x86_64): sparsevb_0.1.1.tgz, r-release (x86_64): sparsevb_0.1.1.tgz, r-oldrel (x86_64): sparsevb_0.1.1.tgz
Old sources:	sparsevb archive

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