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:
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')) |
Bug tracker:https://gitlab.com/gclara/varpack
Last updated 4 years agofrom:5dae9940c0. Checks:OK: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
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Doc / Vignettes | OK | Dec 01 2024 |
R-4.5-linux-x86_64 | NOTE | Dec 01 2024 |
Exports:svb.fit
Dependencies:adaptMCMCcodacodetoolsforeachglmnetintervalsiteratorslatticeMASSMatrixramcmcRcppRcppArmadilloRcppEigenRcppEnsmallenselectiveInferenceshapesurvival
Readme and manuals
Help Manual
Help page | Topics |
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sparsevb: Spike-and-Slab Variational Bayes for Linear and Logistic Regression | sparsevb-package sparsevb |
Fit Approximate Posteriors to Sparse Linear and Logistic Models | svb.fit |