ml.cv: Multicore l.cv for heavy tasks
In anthonyyazdani/OOS: GLM's out of sample (OOS) performances

Multicore l.cv for heavy tasks

ml.cv(
  exprs,
  df,
  family,
  thresh = FALSE,
  statistic = "Accuracy",
  number = 10,
  repeats = 10,
  cl = detectCores()
)

`exprs`	List of GLM formulas.
`df`	Data.frame.
`family`	Object : a description of the error distribution.
`thresh`	If TRUE, an optimal threshold is found based on the 'statistic' criterion.
`statistic`	A character indicating which statistic to maximize. Default is "Accuracy". You can use c('Sensitivity', 'Specificity', 'Pos Pred Value', 'Neg Pred Value', 'Precision', 'Accuracy', 'Balanced Accuracy', 'F1', 'Detection Rate', 'Detection Prevalence', 'Prevalence', 'Recall', 'Kappa', 'J', 'Dist').
`number`	The number of folds.
`repeats`	The number of complete sets of folds to compute.
`cl`	Number of CPU cores on the current host.