abess-package

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 tools:::Rd_expr_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 tools:::Rd_expr_doi("10.1287/ijoc.2022.1241") and sure independence screening tools:::Rd_expr_doi("10.1111/j.1467-9868.2008.00674.x") are also provided. package

See Also

Useful links:

• https://github.com/abess-team/abess
• https://abess-team.github.io/abess/
• https://abess.readthedocs.io
• Report bugs at https://github.com/abess-team/abess/issues

Author(s)

Maintainer : Jin Zhu zhuj37@mail2.sysu.edu.cn (ORCID)

Authors:

• Zezhi Wang homura@mail.ustc.edu.cn
• Liyuan Hu huly5@mail2.sysu.edu.cn
• Junhao Huang huangjh256@mail2.sysu.edu.cn
• Kangkang Jiang jiangkk3@mail2.sysu.edu.cn
• Yanhang Zhang zhangyh98@ruc.edu.cn
• Borui Tang tangborui@mail.ustc.edu.cn
• Shiyun Lin shiyunlin@stu.pku.edu.cn
• Junxian Zhu adaizjx@163.com
• Canhong Wen wencanhong@gmail.com
• Heping Zhang heping.zhang@yale.edu (ORCID)
• Xueqin Wang wangxq20@ustc.edu.cn (ORCID)

Other contributors:

• spectra contributors (Spectra implementation) [copyright holder]