Joint Feature Screening via Sparse MLE
Extract coefficients from fitted model
Data simulator for high-dimensional GLMs
Extract log-likelihood
Plots to visualize the post-screening selection
Plots to visualize SMLE screening
Prediction based on SMLE screening and selection
Print an object
Elaborative post-screening selection with SMLE
Joint SMLE-screening for generalized linear models
Joint feature screening via sparse maximum likelihood estimation for G...
Summarize SMLE-screening and selection
Extract and adjust voting from SMLE selection
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)<doi:10.1080/01621459.2013.879531> 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)<doi:10.18637/jss.v115.i08>.