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: 0.8-7
Depends: R (≥ 3.2.4)
Imports: glmnet, ncvreg, survival
Published: 2019-11-20
Author: Yang Feng [aut, cre], Jianqing Fan [aut], Diego Franco Saldana [aut], Yichao Wu [aut], Richard Samworth [aut]
Maintainer: Yang Feng <yang.feng at nyu.edu>
License: GPL-2
NeedsCompilation: no
Citation: SIS citation info
In views: MachineLearning
CRAN checks: SIS results

Downloads:

Reference manual: SIS.pdf
Package source: SIS_0.8-7.tar.gz
Windows binaries: r-devel: SIS_0.8-6.zip, r-devel-gcc8: SIS_0.8-6.zip, r-release: SIS_0.8-7.zip, r-oldrel: SIS_0.8-6.zip
OS X binaries: r-release: SIS_0.8-6.tgz, r-oldrel: SIS_0.8-6.tgz
Old sources: SIS archive

Reverse dependencies:

Reverse imports: equSA, SILM
Reverse suggests: SuperLearner

Linking:

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