BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models

Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang

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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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