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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 |
| Author: | Qianxiang Zang [aut, cre], Chen Xu [aut], Kelly Burkett [aut], |
| Maintainer: | Qianxiang Zang <qzang023 at uottawa.ca> |
| 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: SMLE_2.1-1.zip, r-release: SMLE_2.1-1.zip, r-oldrel: SMLE_2.1-1.zip |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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