Provides several functions to simplify using the 'glmnet' package: a) converting data frames into matrices ready for 'glmnet'; b) imputing missing variables multiple times; c) fitting and applying prediction models straightforwardly; d) assigning observations to folds in a balanced way; e) cross-validate the models and estimating the accuracy and calibration; f) selecting the most representative model across imputations and folds; and g) getting the count and relevance of the model regressors; as described in several publications: Solanes et al. (2022)