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: |
2.0-2 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
glmnet, matrixcalc, mvnfast |
| Published: |
2021-12-09 |
| 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 |
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