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sparsevb

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

v0.1.1 · Jan 24, 2025 · GPL (>= 3)

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

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Apr 22, 2026
ERROR 13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Apr 18, 2026
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Dependency Network

Dependencies Reverse dependencies Rcpp selectiveInference glmnet (>= 4.0-2) sparsevb

Version History

3 tracked
new 0.1.1 Mar 10, 2026
updated 0.1.1 ← 0.1.0 diff Jan 23, 2025
new 0.1.0 Jan 14, 2021