Package: sparsevb
Type: Package
Title: Spike-and-Slab Variational Bayes for Linear and Logistic
        Regression
Version: 0.1.0
Date: 2021-1-04
Authors@R: c(person("Gabriel", "Clara", email = "gabriel.j.clara@gmail.com", role = c("aut", "cre")), person("Botond", "Szabo", role = "aut"), person("Kolyan", "Ray", role = "aut"))
Author: Gabriel Clara [aut, cre],
  Botond Szabo [aut],
  Kolyan Ray [aut]
Maintainer: Gabriel Clara <gabriel.j.clara@gmail.com>
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 (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <arXiv:2010.11665>.
BugReports: https://gitlab.com/gclara/varpack/-/issues
License: GPL (>= 3)
Imports: Rcpp (>= 1.0.5), selectiveInference (>= 1.2.5), glmnet (>=
        4.0-2), stats
LinkingTo: Rcpp, RcppArmadillo, RcppEnsmallen
SystemRequirements: C++11
Encoding: UTF-8
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-01-12 04:11:21 UTC; gclara
Repository: CRAN
Date/Publication: 2021-01-15 09:20:02 UTC
Built: R 4.1.0; x86_64-apple-darwin17.0; 2021-05-27 05:53:09 UTC; unix
Archs: sparsevb.so.dSYM
