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  [aut,
cre],
Kangkang Jiang [aut],
Yanhang Zhang [aut],
Liyuan Hu [aut],
Junxian Zhu [aut],
Canhong Wen [aut],
Heping Zhang
[aut],
Xueqin Wang [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 |
Downloads:
| 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 |
Linking:
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