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).
- Version0.1.1
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release01/24/2025
Team
Gabriel Clara
MaintainerShow author detailsKolyan Ray
Show author detailsRolesAuthorBotond Szabo
Show author detailsRolesAuthor
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