CBDA_Validation: CBDA Validation function for Compressive Big Data Analytics
In CBDA: Compressive Big Data Analytics

This CBDA function generates *max_covs - min_covs* nested models based on the ranking returned by the *Consolidation* function. It also consolidates all the *max_covs - min_covs* workspaces into a single one.

CBDA_Validation(label = "CBDA_package_test", alpha = 0.2, Kcol_min = 5,
  Kcol_max = 15, Nrow_min = 30, Nrow_max = 50, misValperc = 0,
  M = 3000, N_cores = 1, top = 1000, workspace_directory = tempdir(),
  max_covs = 100, min_covs = 5)

`label`	This is the label appended to RData workspaces generated within the CBDA calls
`alpha`	Percentage of the Big Data to hold off for Validation
`Kcol_min`	Lower bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR)
`Kcol_max`	Upper bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR)
`Nrow_min`	Lower bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR)
`Nrow_max`	Upper bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR)
`misValperc`	Percentage of missing values to introduce in BigData (used just for testing, to mimic real cases).
`M`	Number of the BigData subsets on which perform Knockoff Filtering and SuperLearner feature mining
`N_cores`	Number of Cores to use in the parallel implementation
`top`	Top predictions to select out of the M
`workspace_directory`	Directory where the results and workspaces are saved
`max_covs`	Top features to display and include in the Validation Step where nested models are tested
`min_covs`	Minimum number of top features to include in the initial model for the Validation Step