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 Gaussian, and multinomial regression. 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: 0.1.0
Depends: R (≥ 2.10)
Imports: Rcpp, MASS, methods, Matrix
LinkingTo: Rcpp, RcppEigen
Suggests: testthat, knitr, rmarkdown
Published: 2021-04-21
Author: Jin Zhu ORCID iD [aut, cre], Kangkang Jiang [aut], Yanhang Zhang [aut], Liyuan Hu [aut], Junxian Zhu [aut], Canhong Wen [aut], Heping Zhang ORCID iD [aut], Xueqin Wang ORCID iD [aut]
Maintainer: Jin Zhu <zhuj37 at mail2.sysu.edu.cn>
BugReports: https://github.com/abess-team/abess/issues
License: GPL (≥ 3)
URL: https://github.com/abess-team/abess, https://abess-team.github.io/abess/
NeedsCompilation: yes
SystemRequirements: C++11
Citation: abess citation info
Materials: README NEWS
CRAN checks: abess results


Reference manual: abess.pdf
Vignettes: An Introduction to abess
Package source: abess_0.1.0.tar.gz
Windows binaries: r-devel: abess_0.1.0.zip, r-devel-UCRT: abess_0.1.0.zip, r-release: abess_0.1.0.zip, r-oldrel: abess_0.1.0.zip
macOS binaries: r-release (arm64): abess_0.1.0.tgz, r-release (x86_64): abess_0.1.0.tgz, r-oldrel: abess_0.1.0.tgz


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