Functions to Simplify the Use of 'glmnet' for Machine Learning

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Documentation for package ‘easy.glmnet’ version 1.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

assign.folds	Assign observations to folds in a balanced way
coef.glmnet_fit	Obtain and use a glmnet prediction model
cv	Conduct cross-validation
data.frame2glmnet.matrix	Convert a data.frame into a matrix ready for glmnet
data.frame2glmnet.matrix_fit	Convert a data.frame into a matrix ready for glmnet
glmnet_fit	Obtain and use a glmnet prediction model
glmnet_get.items.count	Get the relevance or count of the model items
glmnet_get.items.relevance	Get the relevance or count of the model items
glmnet_get.main.model	Get the main glmnet model across imputations and folds
glmnet_predict	Obtain and use a glmnet prediction model
impute.glmnet.matrix	Impute missing variables in a glmnet matrix multiple times
impute.glmnet.matrix_fit	Impute missing variables in a glmnet matrix multiple times
new_patients	Simulated data of patients followed for relapse
patients	Simulated data of patients followed for relapse
predict.glmnet_fit	Obtain and use a glmnet prediction model
print.cv	Conduct cross-validation
print.glmnet_fit	Obtain and use a glmnet prediction model
simple.accuracy	Estimate accuracy in unicentric machine learning studies
simple.accuracy.test	Compare the accuracy of two sets of predictions in unicentric studies
simple.calibration	Estimate the calibration of the predictions
surv2binary	Convert a "Surv" object into binary variables at different time points