Joint Feature Screening via Sparse MLE

Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Sparse Maximal Likelihood Estimator (SMLE) (Xu and Chen (2014)) provides an efficient implementation for the joint feature screening method on high-dimensional generalized linear models. It also conducts a post-screening selection based on a user-specified selection criterion. The algorithm uses iterative hard thresholding along with parallel computing.


Reference manual

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0.4.1 by Qianxiang Zang, 5 months ago

Browse source code at

Authors: Qianxiang Zang , Chen Xu , Kelly Burkett

Documentation:   PDF Manual  

GPL-2 license

Imports foreach, mvnfast, doParallel

Depends on glmnet

See at CRAN