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


Reference manual

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0.1.0 by Gabriel Clara, a year ago

Report a bug at https://gitlab.com/gclara/varpack/-/issues

Browse source code at https://github.com/cran/sparsevb

Authors: Gabriel Clara [aut, cre] , Botond Szabo [aut] , Kolyan Ray [aut]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports Rcpp, selectiveInference, glmnet, stats

Linking to Rcpp, RcppArmadillo, RcppEnsmallen

System requirements: C++11

See at CRAN