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>.
| 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 |
| 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 |
| Reference manual: | sparsevb.pdf |
| Package source: | sparsevb_0.1.0.tar.gz |
| Windows binaries: | r-devel: sparsevb_0.1.0.zip, r-devel-UCRT: 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-release (x86_64): sparsevb_0.1.0.tgz, r-oldrel: sparsevb_0.1.0.tgz |
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