abess-package: abess: Fast Best Subset Selection
In abess: Fast Best Subset Selection
abess-package R Documentation
abess: Fast Best Subset Selection
Description
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 \Sexpr[results=rd]{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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1287/ijoc.2022.1241")} and sure independence screening \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1467-9868.2008.00674.x")} are also provided.
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]
See Also
Useful links:
abess documentation built on April 11, 2025, 6:09 p.m.
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| abess-package | R Documentation |
abess: Fast Best Subset Selection
Description
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 \Sexpr[results=rd]{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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1287/ijoc.2022.1241")} and sure independence screening \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1467-9868.2008.00674.x")} are also provided.
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]
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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