# SIS v0.8-8

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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)<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>).

## Functions in SIS

 Name Description tune.fit Using the glmnet and ncvreg packages, fits a Generalized Linear Model or Cox Proportional Hazards Model using various methods for choosing the regularization parameter $$\lambda$$ leukemia.test Gene expression Leukemia testing data set from Golub et al. (1999) prostate.train Gene expression Prostate Cancer training data set from Singh et al. (2002) SIS (Iterative) Sure Independence Screening ((I)SIS) and Fitting in Generalized Linear Models and Cox's Proportional Hazards Models standardize Standardization of High-Dimensional Design Matrices predict.SIS Model prediction based on a fitted SIS object. prostate.test Gene expression Prostate Cancer testing data set from Singh et al. (2002) leukemia.train Gene expression Leukemia training data set from Golub et al. (1999) No Results!