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) 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>
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:, r-release:, r-oldrel:
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 dependencies:

Reverse imports: SparseLearner
Reverse suggests: subsemble, SuperLearner


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