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>.
Package details |
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Author | Gabriel Clara [aut, cre], Botond Szabo [aut], Kolyan Ray [aut] |
Maintainer | Gabriel Clara <gabriel.j.clara@gmail.com> |
License | GPL (>= 3) |
Version | 0.1.0 |
Package repository | View on CRAN |
Installation |
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