abess: Adaptive Best Subset Selection in Polynomial Time

Extremely efficient toolkit for solving the best subset selection problem in linear regression, logistic regression, Poisson regression, Cox proportional hazard model, multiple-response regression, multinomial logistic regression, and (sequential) principal component analysis. It implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times.

Version: 0.3.0
Depends: R (≥ 3.1.0)
Imports: Rcpp, MASS, methods, Matrix
LinkingTo: Rcpp, RcppEigen
Suggests: testthat, knitr, rmarkdown
Published: 2021-09-03
Author: Jin Zhu ORCID iD [aut, cre], Liyuan Hu [aut], Junhao Huang [aut], Kangkang Jiang [aut], Yanhang Zhang [aut], Shiyun Lin [aut], Junxian Zhu [aut], Canhong Wen [aut], Heping Zhang ORCID iD [aut], Xueqin Wang ORCID iD [aut], spectra contributors [cph] (Spectra implementation)
Maintainer: Jin Zhu <zhuj37 at mail2.sysu.edu.cn>
BugReports: https://github.com/abess-team/abess/issues
License: GPL (≥ 3) | file LICENSE
Copyright: see file COPYRIGHTS
URL: https://github.com/abess-team/abess, https://abess-team.github.io/abess/, https://abess.readthedocs.io
NeedsCompilation: yes
SystemRequirements: C++11
Citation: abess citation info
Materials: README NEWS
In views: MachineLearning
CRAN checks: abess results

Documentation:

Reference manual: abess.pdf
Vignettes: An Introduction to abess

Downloads:

Package source: abess_0.3.0.tar.gz
Windows binaries: r-devel: abess_0.3.0.zip, r-devel-UCRT: abess_0.3.0.zip, r-release: abess_0.3.0.zip, r-oldrel: abess_0.3.0.zip
macOS binaries: r-release (arm64): abess_0.3.0.tgz, r-release (x86_64): abess_0.3.0.tgz, r-oldrel: abess_0.3.0.tgz
Old sources: abess archive

Linking:

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