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

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.

To appear in Journal of Statistical Software

References in corpus (2)

• A Constructive Approach to High-dimensional Regression
• On the monotone and primal-dual active set schemes for ${\ell }^p$-type problems, $p\in (0,1]$

Cited by in corpus (1)

• Image denoising via K-SVD with primal-dual active set algorithm