Package: sparsevb 0.1.0

Gabriel Clara

sparsevb: 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) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <arxiv:2010.11665>.

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

sparsevb_0.1.0.tar.gz
sparsevb_0.1.0.tar.gz(r-4.5-noble)sparsevb_0.1.0.tar.gz(r-4.4-noble)
sparsevb_0.1.0.tgz(r-4.4-emscripten)sparsevb_0.1.0.tgz(r-4.3-emscripten)
sparsevb.pdf |sparsevb.html
sparsevb/json (API)

# Install 'sparsevb' in R:
install.packages('sparsevb', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://gitlab.com/gclara/varpack

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

openblascppopenmp

1.00 score 8 scripts 152 downloads 1 exports 18 dependencies

Last updated 4 years agofrom:5dae9940c0. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 01 2024
R-4.5-linux-x86_64NOTEDec 01 2024

Exports:svb.fit

Dependencies:adaptMCMCcodacodetoolsforeachglmnetintervalsiteratorslatticeMASSMatrixramcmcRcppRcppArmadilloRcppEigenRcppEnsmallenselectiveInferenceshapesurvival