combss: Continuous Optimisation Towards Best Subset Selection

Best subset selection in generalised linear models via continuous optimisation. Reformulates the NP-hard discrete subset selection problem as a continuous optimisation over the hypercube [0,1]^p, solved via a Frank-Wolfe homotopy algorithm with closed-form ridge inner solves. Supports linear (Gaussian), binary logistic, and multinomial regression. For methodological details see Moka, Liquet, Zhu and Muller (2024) <doi:10.1007/s11222-024-10387-8> and Mathur, Liquet, Muller and Moka (2026) <doi:10.48550/arXiv.2603.21952>.

Version:	0.1.0
Imports:	glmnet (≥ 4.0), stats
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-05-11
DOI:	10.32614/CRAN.package.combss
Author:	Benoit Liquet [aut, cre], Anant Mathur [aut], Sarat Moka [aut]
Maintainer:	Benoit Liquet <benoit.liquet at univ-pau.fr>
License:	GPL-3
URL:	https://github.com/benoit-liquet/combss
NeedsCompilation:	no
CRAN checks:	combss results

Documentation:

Reference manual:	combss.html , combss.pdf
Vignettes:	Best subset selection with combss (source, R code)

Downloads:

Package source:	combss_0.1.0.tar.gz
Windows binaries:	r-devel: combss_0.1.0.zip, r-release: combss_0.1.0.zip, r-oldrel: combss_0.1.0.zip
macOS binaries:	r-release (arm64): combss_0.1.0.tgz, r-oldrel (arm64): combss_0.1.0.tgz, r-release (x86_64): combss_0.1.0.tgz, r-oldrel (x86_64): combss_0.1.0.tgz

Linking:

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