SIS v0.8-6


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

Functions in SIS

Name Description
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 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)
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Last month downloads


Date 2018-02-13
License GPL-2
RoxygenNote 6.0.1
NeedsCompilation no
Packaged 2018-02-13 07:24:38 UTC; yangfeng
Repository CRAN
Date/Publication 2018-02-13 23:52:33 UTC

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