allMethods              Includes machine learning models used for the
                        mlComb function
availableMethods        Available classification/regression methods in
                        'dtComb'
dtComb                  dtComb: A Comprehensive R Library for Combining
                        Diagnostic Tests
exampleData1            Examples data for the dtComb package
exampleData2            A data set containing the carriers of a rare
                        genetic disorder for 120 samples.
exampleData3            A simulation data containing 250 diseased and
                        250 healthy individuals.
helper_PCL              Helper function for PCL method.
helper_PT               Helper function for PT method.
helper_TS               Helper function for TS method.
helper_minimax          Helper function for minimax method.
helper_minmax           Helper function for minmax method.
kappa.accuracy          Calculate Cohen's kappa and accuracy.
linComb                 Combine two diagnostic tests with several
                        linear combination methods.
mathComb                Combine two diagnostic tests with several
                        mathematical operators and distance measures.
mlComb                  Combine two diagnostic tests with Machine
                        Learning Algorithms.
nonlinComb              Combine two diagnostic tests with several
                        non-linear combination methods.
plotComb                Plot the combination scores using the training
                        model
predict.dtComb          Predict combination scores and labels for new
                        data sets using the training model
print_train             Print the summary of linComb, nonlinComb,
                        mlComb and mathComb functions.
rocsum                  Generate ROC curves and related statistics for
                        the given markers and Combination score.
std.test                Standardization according to the training model
                        parameters.
std.train               Standardization according to the chosen method.
transform_math          Mathematical transformations for biomarkers.
