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: 0.7-6
Depends: R (≥ 3.1.1), glmnet, ncvreg, survival
Published: 2015-11-06
Author: Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu
Maintainer: Diego Franco Saldana <diego at>
BugReports: NA
License: GPL-2
NeedsCompilation: no
In views: MachineLearning
CRAN checks: SIS results


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

Reverse dependencies:

Reverse imports: NHMSAR, SparseLearner
Reverse suggests: subsemble, SuperLearner