BootValidation (0.1.5)

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) .) This package provides bootstrap validation for the linear, logistic, multinomial and cox 'glmnet' models.

Maintainer: Antonio Jose Canada Martinez
Author(s): Antonio Jose Canada Martinez

License: GPL (>= 2)

Uses: glmnet, pbapply, pROC, risksetROC, survival

Released almost 2 years ago.