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.


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install.packages("SMLE")

0.4.1 by Qianxiang Zang, 5 months ago


Browse source code at https://github.com/cran/SMLE


Authors: Qianxiang Zang , Chen Xu , Kelly Burkett


Documentation:   PDF Manual  


GPL-2 license


Imports foreach, mvnfast, doParallel

Depends on glmnet


See at CRAN