cv_grpenet: Cross Validate Group Elastic Net Regression
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Cross Validate Group Elastic Net Regression

cv_grpenet(
  x,
  y,
  idx,
  cv.method = "boot632",
  nfolds = 5,
  nrep = 4,
  tunlen = 10,
  crit = "MAE",
  max.c = 8
)

`x`	the model matrix
`y`	the outcome
`cv.method`	preferably one of "boot632" (the default), "cv", or "repeatedcv".
`nfolds`	the number of bootstrap or cross-validation folds to use. defaults to 5.
`nrep`	the number of repetitions for cv.method = "repeatedcv". defaults to 4.
`tunlen`	the number of values for the unknown hyperparameter to test. defaults to 10.
`crit`	the criterion by which to evaluate the model performance. must be one of "MAE" (the default) or "MSE".
`max.c`	the largest value of the constant for calculating lambda. defaults to 8, but may be adjusted. for example, if the error metric becomes constant after a certain value of C, it may be advisable to lower max.c to a smaller value to obtain a more fine-grained grid over the plausible values.