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) <doi:10.48550/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
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
SystemRequirements:	C++11
CRAN checks:	sparsevb results

Documentation:

Reference manual:

sparsevb.pdf

Downloads:

Package source:	sparsevb_0.1.0.tar.gz
Windows binaries:	r-devel: sparsevb_0.1.0.zip, r-release: sparsevb_0.1.0.zip, r-oldrel: sparsevb_0.1.0.zip
macOS binaries:	r-release (arm64): sparsevb_0.1.0.tgz, r-oldrel (arm64): sparsevb_0.1.0.tgz, r-release (x86_64): sparsevb_0.1.0.tgz, r-oldrel (x86_64): sparsevb_0.1.0.tgz

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