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: | 0.8-6 |
| Depends: | R (≥ 3.2.4) |
| Imports: | glmnet, ncvreg, survival |
| Published: | 2018-02-13 |
| Author: | Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu |
| Maintainer: | Yang Feng <yang.feng at columbia.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | SIS citation info |
| In views: | MachineLearning |
| CRAN checks: | SIS results |
| Reference manual: | SIS.pdf |
| Package source: | SIS_0.8-6.tar.gz |
| Windows binaries: | r-devel: SIS_0.8-6.zip, r-release: SIS_0.8-6.zip, r-oldrel: SIS_0.8-6.zip |
| OS X El Capitan binaries: | r-release: SIS_0.8-6.tgz |
| OS X Mavericks binaries: | r-oldrel: SIS_0.8-4.tgz |
| Old sources: | SIS archive |
| Reverse imports: | SparseLearner |
| Reverse suggests: | subsemble, SuperLearner |
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