SMLE: 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)<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.

Version: 2.1-1
Depends: R (≥ 4.0.0)
Imports: glmnet, matrixcalc, mvnfast
Suggests: testthat (≥ 3.0.0)
Published: 2024-02-12
DOI: 10.32614/CRAN.package.SMLE
Author: Qianxiang Zang [aut, cre], Chen Xu [aut], Kelly Burkett [aut],
Maintainer: Qianxiang Zang <qzang023 at>
License: GPL-3
NeedsCompilation: no
Citation: SMLE citation info
CRAN checks: SMLE results


Reference manual: SMLE.pdf


Package source: SMLE_2.1-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): SMLE_2.1-1.tgz, r-oldrel (arm64): SMLE_2.1-1.tgz, r-release (x86_64): SMLE_2.1-1.tgz, r-oldrel (x86_64): SMLE_2.1-1.tgz
Old sources: SMLE archive


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