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.

AuthorJianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu
Date of publication2016-10-12 09:00:48
MaintainerYang Feng <yang.feng@columbia.edu>
LicenseGPL-2
Version0.8-3
http://www.stat.columbia.edu/~yangfeng/pubs/jss1375.pdf

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Files

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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