compute_aurocs: Efficient computation of AUROCs (vectorized for predictors...
In gillislab/MetaMarkers: Extract robust markers from multiple datasets

Efficient computation of AUROCs (vectorized for predictors and categories).

1	compute_aurocs(predictors, label_matrix, compute_tie_correction = FALSE)

`predictors`	Matrix where each column is a predictor and each row is a sample.
`label_matrix`	One-hot encoded matrix where columns are categories and each row is a sample. The number of rows must be identical to the number of rows in predictors. 1 indicates that the sample on this row belongs to the category on this column.
`compute_tie_correction`	Boolean. If TRUE, for each AUROC, compute classical tie correction (only useful for p-value computation).