Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) and all of its variants in generalized linear models and the Cox proportional hazards model.
| Version: | 0.7-6 |
| Depends: | R (≥ 3.1.1), glmnet, ncvreg, survival |
| Published: | 2015-11-06 |
| Author: | Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu |
| Maintainer: | Diego Franco Saldana <diego at stat.columbia.edu> |
| BugReports: | NA |
| License: | GPL-2 |
| URL: | NA |
| NeedsCompilation: | no |
| In views: | MachineLearning |
| CRAN checks: | SIS results |
| Reference manual: | SIS.pdf |
| Package source: | SIS_0.7-6.tar.gz |
| Windows binaries: | r-devel: SIS_0.7-6.zip, r-release: SIS_0.7-6.zip, r-oldrel: SIS_0.7-6.zip |
| OS X Mavericks binaries: | r-release: SIS_0.7-6.tgz, r-oldrel: SIS_0.7-6.tgz |
| Old sources: | SIS archive |
| Reverse imports: | NHMSAR, SparseLearner |
| Reverse suggests: | subsemble, SuperLearner |