Joint Feature Screening via Sparse MLE

Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014) proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion. Zang, Xu, and Burkett (2025).