BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models
Author(s) -
Canhong Wen,
Aijun Zhang,
Shijie Quan,
Xueqin Wang
Publication year - 2020
Publication title -
journal of statistical software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.636
H-Index - 145
ISSN - 1548-7660
DOI - 10.18637/jss.v094.i04
Subject(s) - selection (genetic algorithm) , r package , computer science , set (abstract data type) , logistic regression , hazard , algorithm , mathematical optimization , mathematics , machine learning , computational science , programming language , organic chemistry , chemistry
We introduce a new R package, BeSS, for solving the best subset selection problem in linear, logistic and Cox's proportional hazard (CoxPH) models. It utilizes a highly efficient active set algorithm based on primal and dual variables, and supports sequential and golden search strategies for best subset selection. We provide a C++ implementation of the algorithm using an Rcpp interface. We demonstrate through numerical experiments based on enormous simulation and real datasets that the new BeSS package has competitive performance compared to other R packages for best subset selection purposes.
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