SMLE v0.3.1
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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.
Functions in SMLE
| Name | Description | |
| print.selection | Print a selection object from smle_select | |
| plot.smle | Plots to visualize the SMLE screening step | |
| print.sdata | Print function for simulated data | |
| Gen_Data | Data simulator for high-dimensional GLMs | |
| smle-package | Joint SMLE-screening for generalized linear models | |
| print.smle | Print a SMLE object from SMLE | |
| smle_select | Elaborative feature selection with SMLE | |
| SMLE | Joint feature screening via sparse maximum likelihood estimation for GLMs | |
| plot.selection | Plots to visualize the selection steps | |
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Details
| License | GPL-2 |
| 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 |
| imports | doParallel , foreach , glmnet , mnormt |
| depends | R (>= 3.2.4) |
| Contributors | Kelly Burkett, Qianxiang Zang, Chen Xu |
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[](http://www.rdocumentation.org/packages/SMLE)