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SIS (version 0.8-8)

Sure Independence Screening

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)).

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Version

Install

install.packages('SIS')

Monthly Downloads

1,312

Version

0.8-8

License

GPL-2

Maintainer

Yang Feng

Last Published

January 27th, 2020

Functions in SIS (0.8-8)

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)