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 (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <arXiv:2010.11665>.

Version: 0.1.0
Imports: Rcpp (≥ 1.0.5), selectiveInference (≥ 1.2.5), glmnet (≥ 4.0-2), stats
LinkingTo: Rcpp, RcppArmadillo, RcppEnsmallen
Published: 2021-01-15
Author: Gabriel Clara [aut, cre], Botond Szabo [aut], Kolyan Ray [aut]
Maintainer: Gabriel Clara <gabriel.j.clara at>
License: GPL (≥ 3)
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: sparsevb results


Reference manual: sparsevb.pdf
Package source: sparsevb_0.1.0.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sparsevb_0.1.0.tgz, r-release (x86_64): sparsevb_0.1.0.tgz, r-oldrel: sparsevb_0.1.0.tgz


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