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 a user-specified selection criterion. The algorithm uses iterative hard thresholding along with parallel computing.
| Version: | 0.4.1 |
| Depends: | R (≥ 4.0.0), glmnet (≥ 4.0) |
| Imports: | foreach, mvnfast, doParallel |
| Published: | 2020-06-24 |
| Author: | Qianxiang Zang,Chen Xu,Kelly Burkett |
| Maintainer: | Qianxiang Zang <qzang023 at uottawa.ca> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | SMLE results |
| Reference manual: | SMLE.pdf |
| Package source: | SMLE_0.4.1.tar.gz |
| Windows binaries: | r-devel: SMLE_0.4.1.zip, r-release: SMLE_0.4.1.zip, r-oldrel: SMLE_0.3.1.zip |
| macOS binaries: | r-release: SMLE_0.4.1.tgz, r-oldrel: SMLE_0.3.1.tgz |
| Old sources: | SMLE archive |
Please use the canonical form https://CRAN.R-project.org/package=SMLE to link to this page.