Imports: backports, checkmate (>= 2.0.0), data.table (>= 1.13.6), mlr3

Suggests: curl, lgr, formattable, tidyverse, PracTools, mlr3learners,

mlr3oml, neuralnet, paradox, knitr, ranger, xgboost, covr,

Description: A number of biomedical problems involve performing many hypothesis tests, with an attendant need to apply stringent thresholds. Often the data take the form of a series of predictor vectors, each of which must be compared with a single response vector, perhaps with nuisance covariates. Parametric tests of association are often used, but can result in inaccurate type I error at the extreme thresholds, even for large sample sizes. Furthermore, standard two-sided testing can reduce power compared to the doubled p-value, due to asymmetry in the null distribution. Exact (permutation) testing approaches are attractive, but can be computationally intensive and cumbersome. MCC is an approximation to exact association testing of two vectors that is accurate and fast enough for standard use in high-throughput settings, and can easily provide standard two-sided or doubled p-values.

Description: It contains six robust diagnostic accuracy methods to evaluate three or four category classifiers.
Hypervolume Under Manifold (HUM), described in the paper:
Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in the paper:
Polytomous Discrimination Index (PDI), described in the paper:

Description: It contains six common multi-category classification accuracy evaluation measures:
Hypervolume Under Manifold (HUM), described in
Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>.
Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in
Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>.
Polytomous Discrimination Index (PDI), described in

We described all these above measures and our mcca package in