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SIS (version 0.8-6)

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) and all of its variants in generalized linear models and the Cox proportional hazards model.

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Version

Install

install.packages('SIS')

Monthly Downloads

1,130

Version

0.8-6

License

GPL-2

Maintainer

Yang Feng

Last Published

February 13th, 2018

Functions in SIS (0.8-6)

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
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 \(\lambda\)
prostate.test

Gene expression Prostate Cancer testing data set from Singh et al. (2002)
prostate.train

Gene expression Prostate Cancer training data set from Singh et al. (2002)
leukemia.train

Gene expression Leukemia training data set from Golub et al. (1999)
predict.SIS

Model prediction based on a fitted SIS object.
leukemia.test

Gene expression Leukemia testing data set from Golub et al. (1999)