SMLE: An R Package for Joint Feature Screening in Ultrahigh-dimensional GLMs

TL;DR

The paper introduces an R package for SMLE, a feature screening method that efficiently handles joint effects in ultrahigh-dimensional generalized linear models, demonstrated through extensive numerical examples.

Contribution

The paper develops and releases a user-friendly R package for SMLE, enabling joint feature screening in GLMs with flexible parameters and post-screening selection options.

Findings

SMLE package performs well in numerical tests.

It effectively incorporates joint effects among features.

The package offers flexible screening and selection options.

Abstract

The sparsity-restricted maximum likelihood estimator (SMLE) has received considerable attention for feature screening in ultrahigh-dimensional regression. SMLE is a computationally convenient method that naturally incorporates the joint effects among features in the screening process. We develop a publicly available R package SMLE, which provides a user-friendly environment to carry out SMLE in generalized linear models. In particular, the package includes functions to conduct SMLE-screening and post-screening selection using SMLE with popular selection criteria such as AIC and (extended) BIC. The package gives users the flexibility in controlling a series of screening parameters and accommodates both numerical and categorical feature input. The usage of the package is illustrated on extensive numerical examples, where the promising performance of SMLE is well observed.