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)).


Reference manual

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0.8-8 by Yang Feng, 2 years ago

Browse source code at

Authors: Yang Feng [aut, cre] , Jianqing Fan [aut] , Diego Franco Saldana [aut] , Yichao Wu [aut] , Richard Samworth [aut]

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

GPL-2 license

Imports glmnet, ncvreg, survival

Imported by RsqMed, SILM, gfiUltra.

Suggested by MFSIS, SuperLearner.

See at CRAN