cv_rmcp: Cross Validate Robust Minimax Concave Penalized Regression
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Cross Validate Robust Minimax Concave Penalized Regression

cv_rmcp(
  formula,
  data,
  cv.method = c("boot632", "boot", "cv", "repeatedcv", "LOOCV"),
  nfolds = 5,
  nrep = 4,
  folds = NULL,
  tunlen = 25,
  crit = c("TauScale2", "RobustMAE", "RobustMSE")
)

`formula`	a model formula
`data`	a training data set
`cv.method`	one of "boot632" (the default), "boot", "cv", "repeatedcv", or "LOOCV".
`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.
`folds`	a vector of pre-set cross-validation or bootstrap folds from caret::createResample or caret::createFolds.
`tunlen`	the number of values for the unknown hyperparameter to test. defaults to 25.
`crit`	the criterion by which to evaluate the model performance. must be one of "TauScale2" (the default), "RobustMAE", or "RobustMSE". "TauScale2" gives the squared tau estimate of the scale of the residuals. "RobustMAE" and "RobustMSE" are the tau estimates of mean absolute and squared errors respectively.