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>.
For details as they pertain to using the package, consult the
svb.fit function help page. Detailed descriptions and
derivations of the variational algorithms with Laplace slabs may be found
in the references.
Maintainer: Gabriel Clara email@example.com
Ray K. and Szabo B. Variational Bayes for high-dimensional linear regression with sparse priors. (2020). Journal of the American Statistical Association.
Ray K., Szabo B., and Clara G. Spike and slab variational Bayes for high dimensional logistic regression. (2020). Advances in Neural Information Processing Systems 33.
Report bugs at https://gitlab.com/gclara/varpack/-/issues
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