Ancamar/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, logistic, multinomial and cox 'glmnet' models as well as lm and glm models.

install.packages("remotes")
remotes::install_github("Ancamar/BootValidation")