abess
Fast Best Subset Selection
Extremely efficient toolkit for solving the best subset selection problem https://www.jmlr.org/papers/v23/21-1060.html. This package is its R interface. The package 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 for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection doi:10.1287/ijoc.2022.1241 and sure independence screening doi:10.1111/j.1467-9868.2008.00674.x are also provided.
- GitHub
- https://abess-team.github.io/abess/
- https://abess.readthedocs.io
- File a bug report
- abess results
- abess.pdf
- Version0.4.10
- R versionR (≥ 3.1.0)
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- abess citation info
- Last release04/05/2025
Documentation
Team
Jin Zhu
MaintainerShow author detailsYanhang Zhang
Show author detailsRolesAuthorJunhao Huang
Show author detailsRolesAuthorLiyuan Hu
Show author detailsRolesAuthorCanhong Wen
Show author detailsRolesAuthorHeping Zhang
Zezhi Wang
Show author detailsRolesAuthorspectra contributors
Show author detailsRolesCopyright holderShiyun Lin
Show author detailsRolesAuthorBorui Tang
Show author detailsRolesAuthorKangkang Jiang
Show author detailsRolesAuthorXueqin Wang
Junxian Zhu
Show author detailsRolesAuthor
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Dependencies
- Imports3 packages
- Suggests3 packages
- Linking To2 packages
- Reverse Suggests1 package