BranchGLM: Efficient Branch and Bound Variable Selection for GLMs using 'RcppArmadillo'

Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'.

Version: 1.2.0
Depends: R (≥ 3.3.0)
Imports: Rcpp (≥ 1.0.7), methods
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2022-07-23
Author: Jacob Seedorff [aut, cre]
Maintainer: Jacob Seedorff <jwseedorff at uiowa.edu>
BugReports: https://github.com/JacobSeedorff21/BranchGLM/issues
License: Apache License (≥ 2)
URL: https://github.com/JacobSeedorff21/BranchGLM
NeedsCompilation: yes
CRAN checks: BranchGLM results

Documentation:

Reference manual: BranchGLM.pdf
Vignettes: BranchGLM Vignette

Downloads:

Package source: BranchGLM_1.2.0.tar.gz
Windows binaries: r-devel: BranchGLM_1.2.0.zip, r-release: BranchGLM_1.2.0.zip, r-oldrel: BranchGLM_1.2.0.zip
macOS binaries: r-release (arm64): BranchGLM_1.2.0.tgz, r-oldrel (arm64): BranchGLM_1.2.0.tgz, r-release (x86_64): BranchGLM_1.2.0.tgz, r-oldrel (x86_64): BranchGLM_1.2.0.tgz
Old sources: BranchGLM archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BranchGLM to link to this page.