Package: BootValidation
Type: Package
Title: Adjusting for Optimism in 'glmnet' Regression using
        Bootstrapping
Version: 0.1.3
Author: Antonio Jose Canada Martinez
Maintainer: Antonio Jose Canada Martinez <ancamar2@gmail.com>
Description: Main objective of a predictive model is to provide accurated predictions of a new observations. 
             Unfortunately we don't know how well the model performs. In addition, at the current era of omic
             data where p >> n, is not reasonable applying internal validation using data-splitting. Under this 
             background a good method to assessing model performance is applying internal bootstrap validation 
             (Harrell Jr, Frank E (2015) <doi:10.1007/978-1-4757-3462-1>.) This package provides bootstrap validation
             for the linear and logistic 'glmnet' models.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Imports: glmnet, pbapply, pROC, parallel
RdMacros: Rdpack
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-11-14 13:28:30 UTC; 53257632M
Repository: CRAN
Date/Publication: 2017-11-14 15:30:31 UTC
