cv_genet: Cross validate generalized elastic net tuning parameters
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Cross validate generalized elastic net tuning parameters

cv_genet(
  formula,
  data,
  family = gaussian(),
  cv.method = c("adaptive_boot", "adaptive_cv"),
  nfolds = 15,
  nrep = 4,
  tunlen = 10,
  folds = NULL,
  max.lambda = 3,
  crit = c("MSE", "MAE", "Accuracy", "kappa")
)

`formula`	a model formula
`data`	a training data set
`family`	a glm family
`cv.method`	preferably one of "adaptive_boot" or "adaptive_cv"
`nfolds`	the number of bootstrap or cross-validation folds to use. defaults to 15.
`nrep`	the number of repetitions for cv.method = "repeatedcv". defaults to 4.
`tunlen`	the number of values of lambda to test. defaults to 10.
`folds`	a vector of pre-set cross-validation or bootstrap folds from caret::createResample or caret::createFolds.
`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 lambda to try.