SIS: Sure Independence Screening
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 |
| Author: |
Yang Feng [aut, cre],
Jianqing Fan [aut],
Diego Franco Saldana [aut],
Yichao Wu [aut],
Richard Samworth [aut] |
| Maintainer: |
Yang Feng <yangfengstat at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Citation: |
SIS citation info |
| In views: |
MachineLearning |
| CRAN checks: |
SIS results |
Documentation:
Downloads:
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