Package: SMLE
Title: Joint Feature Screening via Sparse MLE
Version: 0.3.1
Author: Qianxiang Zang,Chen Xu,Kelly Burkett
Maintainer: Qianxiang Zang <qzang023@uottawa.ca>
Imports: foreach, glmnet, mnormt, doParallel
Description: 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. 
License: GPL-2
Depends: R (>= 3.2.4)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Repository: CRAN
Packaged: 2020-05-13 04:44:44 UTC; mac
Date/Publication: 2020-05-18 15:40:03 UTC
