SIS: Sure Independence Screening

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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.

Author
Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu
Date of publication
2016-10-12 09:00:48
Maintainer
Yang Feng <yang.feng@columbia.edu>
License
GPL-2
Version
0.8-3
URLs

View on CRAN

Man pages

leukemia.test
Gene expression Leukemia testing data set from Golub et al....
leukemia.train
Gene expression Leukemia training data set from Golub et al....
predict.SIS
Model prediction based on a fitted SIS object.
prostate.test
Gene expression Prostate Cancer testing data set from Singh...
prostate.train
Gene expression Prostate Cancer training data set from Singh...
SIS
(Iterative) Sure Independence Screening ((I)SIS) and Fitting...
standardize
Standardization of High-Dimensional Design Matrices
tune.fit
Using the 'glmnet' and 'ncvreg' packages, fits a Generalized...

Files in this package

SIS
SIS/inst
SIS/inst/CITATION
SIS/NAMESPACE
SIS/data
SIS/data/prostate.train.RData
SIS/data/leukemia.test.RData
SIS/data/leukemia.train.RData
SIS/data/datalist
SIS/data/prostate.test.RData
SIS/R
SIS/R/SIS.R
SIS/R/predict.SIS.R
SIS/R/subfuns.R
SIS/R/standardize.R
SIS/R/tune.fit.R
SIS/MD5
SIS/DESCRIPTION
SIS/man
SIS/man/prostate.test.Rd
SIS/man/leukemia.train.Rd
SIS/man/prostate.train.Rd
SIS/man/leukemia.test.Rd
SIS/man/tune.fit.Rd
SIS/man/predict.SIS.Rd
SIS/man/standardize.Rd
SIS/man/SIS.Rd