SIS (0.8-8)

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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)) and all of its variants in generalized linear models (Fan and Song (2009)) and the Cox proportional hazards model (Fan, Feng and Wu (2010)).

Maintainer: Yang Feng
Author(s): Yang Feng [aut, cre], Jianqing Fan [aut], Diego Franco Saldana [aut], Yichao Wu [aut], Richard Samworth [aut]

License: GPL-2

Uses: glmnet, ncvreg, survival
Reverse depends: SparseLearner
Reverse suggests: SuperLearner

Released about 1 month ago.

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