Package: combss 0.1.0

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>.

Authors:Benoit Liquet [aut, cre], Anant Mathur [aut], Sarat Moka [aut]

combss_0.1.0.tar.gz
combss_0.1.0.zip(r-4.7)combss_0.1.0.zip(r-4.6)combss_0.1.0.zip(r-4.5)
combss_0.1.0.tgz(r-4.6-any)combss_0.1.0.tgz(r-4.5-any)
combss_0.1.0.tar.gz(r-4.7-any)combss_0.1.0.tar.gz(r-4.6-any)
combss_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
combss/json (API)

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

Bug tracker:https://github.com/benoit-liquet/combss/issues

On CRAN:

Conda:

2.70 score 4 scripts 17 exports 10 dependencies

Last updated from:b7816fe436. Checks:8 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR135
source / vignettesERROR174
linux-release-x86_64ERROR129
macos-release-arm64ERROR114
macos-oldrel-arm64ERROR143
windows-develERROR93
windows-releaseERROR137
windows-oldrelERROR107
wasm-releaseOK101

Exports:calibrate_penalty_schedulecoef.combsscombsscombss_cvcompute_nu_maxfw_loopgrad_envelopeloocv_metricpredict.combssprint.combssrefit_predictresolve_familyscale_design_matrixsolve_inner_binomialsolve_inner_gaussiansolve_inner_multinomialsummary.combss

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival