<b>BeSS</b>: An <i>R</i> Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models
Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang
Abstract
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.
Topics & Concepts
Selection (genetic algorithm)R packageComputer scienceSet (abstract data type)Logistic regressionHazardAlgorithmMathematical optimizationMathematicsMachine learningComputational scienceProgramming languageChemistryOrganic chemistryBayesian Modeling and Causal InferenceFuzzy Systems and OptimizationAdvanced Statistical Methods and Models