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) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).

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Description file content

Package
SIS
Version
0.8-7
Date
2019-11-19
Title
Sure Independence Screening
Depends
R (>= 3.2.4)
Imports
glmnet, ncvreg, survival
Description
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) (Fan and Lv (2008)) and all of its variants in generalized linear models (Fan and Song (2009)) and the Cox proportional hazards model (Fan, Feng and Wu (2010)).
License
GPL-2
RoxygenNote
6.0.1
NeedsCompilation
no
Packaged
2019-11-19 22:11:22 UTC; yangfeng
Author
Yang Feng [aut, cre], Jianqing Fan [aut], Diego Franco Saldana [aut], Yichao Wu [aut], Richard Samworth [aut]
Maintainer
Yang Feng
Repository
CRAN
Date/Publication
2019-11-20 08:50:10 UTC

install.packages('SIS')

0.8-7

26 days ago

Yang Feng

GPL-2

Depends on

R (>= 3.2.4)

Imports

glmnet, ncvreg, survival

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