aIc.coherent            Calculate the subcompositional coherence of
                        samples in a dataset for a given correction.
aIc.dominant            'aIc.dominant' calculates the subcompositional
                        dominance of a sample in a dataset for a given
                        correction. This compares the distances of
                        samples of the full dataset and a subset of the
                        dataset.  This is expected to be true if the
                        transform is behaving rationally in
                        compositional datasets.
aIc.perturb             'aIc.perturb' calculates the perturbation
                        invariance of distance for samples with a given
                        correction. This compares the distances of
                        samples of the full dataset and a the perturbed
                        dataset.  This is expected to be true if the
                        transform is behaving rationally in
                        compositional datasets.
aIc.plot                'aIc.plot' plots the result of the distance
                        tests.
aIc.runExample          'aIc.runExample' loads the associated shiny app
                        This will load the selex example dataset with
                        the default group sizes, the user can upload
                        their own local dataset and adjust groups
                        accordingly.
aIc.scale               'aIc.scale' calculates the scaling invariance
                        of a sample in a dataset for a given
                        correction. This compares the distances of
                        samples of the full dataset and a scaled
                        version of the dataset.  This is expected to be
                        true if the transform is behaving rationally in
                        compositional datasets.
aIc.singular            'aIc.singular' tests for singular data.  This
                        is expected to be true if the transform is
                        behaving rationally in compositional datasets
                        and also true in the case of datasets with more
                        features than samples.
meta16S                 16S rRNA tag-sequencing data
metaTscome              meta-transcriptome data
selex                   Selection-based differential sequence variant
                        abundance dataset
singleCell              single cell transcriptome data
transcriptome           Saccharomyces cerevisiae transcriptome
