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: 0.8-1
Depends: R (≥ 3.2.4)
Imports: glmnet, ncvreg, survival
Published: 2016-08-13
Author: Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu
Maintainer: Yang Feng <yang.feng at columbia.edu>
License: GPL-2
URL: http://www.stat.columbia.edu/~yangfeng/pubs/jss1375.pdf
NeedsCompilation: no
In views: MachineLearning
CRAN checks: SIS results

Downloads:

Reference manual: SIS.pdf
Package source: SIS_0.8-1.tar.gz
Windows binaries: r-devel: SIS_0.8-1.zip, r-release: SIS_0.8-1.zip, r-oldrel: SIS_0.8-1.zip
OS X Mavericks binaries: r-release: SIS_0.8-1.tgz, r-oldrel: SIS_0.8-1.tgz
Old sources: SIS archive

Reverse dependencies:

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

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