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.
|Author||Qianxiang Zang [aut, cre], Chen Xu [aut], Kelly Burkett [aut],|
|Maintainer||Qianxiang Zang <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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