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 that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times.


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install.packages("abess")

0.1.0 by Jin Zhu, 21 days ago


https://github.com/abess-team/abess, https://abess-team.github.io/abess/


Report a bug at https://github.com/abess-team/abess/issues


Browse source code at https://github.com/cran/abess


Authors: 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]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports Rcpp, MASS, methods, Matrix

Suggests testthat, knitr, rmarkdown

Linking to Rcpp, RcppEigen

System requirements: C++11


See at CRAN