SMLE: 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)<doi:10.1080/01621459.2013.879531>) 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 user-specified selection criterion. The algorithm uses iterative hard thresholding along with parallel computing.

Version: 0.3.1
Depends: R (≥ 3.2.4)
Imports: foreach, glmnet, mnormt, doParallel
Published: 2020-05-18
Author: Qianxiang Zang,Chen Xu,Kelly Burkett
Maintainer: Qianxiang Zang <qzang023 at uottawa.ca>
License: GPL-2
NeedsCompilation: no
CRAN checks: SMLE results

Downloads:

Reference manual: SMLE.pdf
Package source: SMLE_0.3.1.tar.gz
Windows binaries: r-devel: SMLE_0.3.1.zip, r-release: SMLE_0.3.1.zip, r-oldrel: SMLE_0.3.1.zip
macOS binaries: r-release: SMLE_0.3.1.tgz, r-oldrel: SMLE_0.3.1.tgz

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