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

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

## Documentation for package ‘easy.glmnet’ version 1.1

- DESCRIPTION file
- 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.relevance | Get the relevance 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
predict.glmnet_fit | Obtain and use a glmnet prediction model
surv2binary | Convert a "Surv" object into binary variables at different time points