BootValidation: Adjusting for Optimism in 'glmnet' Regression using Bootstrapping

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.

Version: 0.1.3
Imports: glmnet, pbapply, pROC, parallel
Published: 2017-11-14
Author: Antonio Jose Canada Martinez
Maintainer: Antonio Jose Canada Martinez <ancamar2 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: BootValidation results


Reference manual: BootValidation.pdf
Package source: BootValidation_0.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: BootValidation_0.1.3.tgz
OS X Mavericks binaries: r-oldrel: BootValidation_0.1.3.tgz


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