tl;dr with the R glmnet package, is it possible to optimize for the area under the precision-recall curve, rather than the area under the ROC curve?

**More details**

I am using the `glmnet` package in `R` to perform elastic-net penalized logistic regression for binary classification on a **severely** class unbalanced dataset, using `type.measure = 'auc'` to optimize the area under the curve (AUC) of the receiver operator characteristic (ROC), during cross-validation to select an elastic-net lambda parameter.

However, with severely imbalanced datasets, it appears that area under the Precision-Recall (PRC) curve may be preferable to ROC AUC; e.g., [Saito 2015](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349800/).

This does not seem to be a `type.measure` option in `cv.glmnet`. Has anyone found a way to use `glmnet` logistic regression with PRC-AUC? If not, how important do people think it is to use PRC and not ROC for a severely class imbalanced target?