BeSS: An R Package for Best Subset Selection in Linear, Logistic and CoxPH Models

TL;DR

The BeSS R package offers an efficient tool for best subset selection across linear, logistic, and CoxPH models, utilizing advanced algorithms and demonstrating competitive performance on large datasets.

Contribution

Introduces the BeSS R package with a novel active set algorithm and C++ implementation for improved subset selection in multiple statistical models.

Findings

Demonstrates competitive performance on large simulation datasets.

Supports multiple models including linear, logistic, and CoxPH.

Employs efficient algorithms with Rcpp for speed.

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 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 purpose.