SIS: Sure Independence Screening (original) (raw)
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) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).
| Version: | 0.8-8 |
|---|---|
| Depends: | R (≥ 3.2.4) |
| Imports: | glmnet, ncvreg, survival |
| Published: | 2020-01-27 |
| DOI: | 10.32614/CRAN.package.SIS |
| Author: | Yang Feng [aut, cre], Jianqing Fan [aut], Diego Franco Saldana [aut], Yichao Wu [aut], Richard Samworth [aut] |
| Maintainer: | Yang Feng |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | SIS citation info |
| In views: | MachineLearning |
| CRAN checks: | SIS results |
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