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.


Reference manual

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0.8-6 by Yang Feng, 10 days ago

Browse source code at

Authors: Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

GPL-2 license

Imports glmnet, ncvreg, survival

Imported by SparseLearner.

Suggested by SuperLearner, subsemble.

See at CRAN