ABCdeep.SingleInMultiple
                        Single-source ABC with a deep-learning
                        (autoencoder) Step 4 (ABCdeep)
ABCdist.SingleInMultiple
                        Single-source ABC with distance accumulation
ABCpp.SingleInMultiple
                        Single-source Aggregating Bundles of Clusters
                        (ABC), C++-accelerated engine
ADC                     Aggregated data clustering
ADEC                    Aggregated data ensemble clustering
BinFeaturesPlot_MultipleData
                        Visualization of characteristic binary features
                        of multiple data sets
BinFeaturesPlot_SingleData
                        Visualization of characteristic binary features
                        of a single data set
BoxPlotDistance         Box plots of one distance matrix categorized
                        against another distance matrix.
CEC                     Complementary ensemble clustering
CVAA                    Cumulative Voting Aggregation (CVAA / W-CVAA)
CharacteristicFeatures
                        Determine the characteristic features of
                        clusters
ChooseCluster           Interactive plot to determine DE Genes and DE
                        features for a specific cluster
Cluster                 Single-source base clustering
ClusterCols             Helper that colours dendrogram leaves by
                        cluster membership
ClusterPlot             Colouring clusters in a dendrogram
ClusteringAggregation   Clustering aggregation
ColorPalette            Create a color palette to be used in the plots
ColorsNames             Function that annotates colors to their names
CompareInteractive      Interactive comparison of single and multiple
                        source clustering results
ComparePlot             Comparison of clustering results over multiple
                        methods
CompareSilCluster       Compares medoid clustering results based on
                        silhouette widths
CompareSvsM             Comparison of clustering results for the single
                        and multiple source clustering.
ConsensusClustering     Voting-based consensus clustering
ContFeaturesPlot        Plot of continuous features
Cyclogram               Comparison of clustering results over multiple
                        results in circular format
DetermineWeight_SilClust
                        Determines an optimal weight for weighted
                        clustering by silhouettes widths.
DetermineWeight_SimClust
                        Determines an optimal weight for weighted
                        clustering by similarity weighted clustering.
DiffGenes               Find differentially expressed genes
DiffGenesSelection      Differential expression for a selection of
                        objects
Distance                Compute a distance matrix
EHC                     Ensemble hierarchical clustering via graph
                        partitioning (METIS / MST)
EnsembleClustering      Ensemble clustering via graph partitioning
                        (CSPA / HGPA / MCLA)
EvidenceAccumulation    Evidence accumulation clustering
                        (co-association)
FeatSelection           Determine the characteristic features of a
                        cluster or selection
FeaturesOfCluster       List all features present in a selected cluster
                        of objects
FindCluster             Find a selection of objects in the output of
                        'ReorderToReference'
FindElement             Find an element in a data structure
FindGenes               Find the genes shared across methods
Geneset.intersect       Intersection over resulting gene sets of
                        'PathwaysIter' function
Geneset.intersectSelection
                        Intersection over resulting gene sets of
                        'PathwaysIter' function for a selection of
                        objects
HBGF                    Hybrid Bipartite Graph Formulation
HeatmapPlot             A heatmap of the comparison of two clustering
                        results.
HeatmapSelection        A function to select a group of objects via the
                        similarity heatmap.
HierarchicalEnsembleClustering
                        Hierarchical ensemble clustering
LUCID                   LUCID: latent unknown clustering integrating
                        omics with an outcome
LabelCols               Helper that colours specific dendrogram leaves
LabelPlot               Coloring specific leaves of a dendrogram
LinkBasedClustering     Link-based cluster ensembles
M_ABCdeep               Multi-source M-ABC with a deep-learning
                        (autoencoder) Step 4 (M-ABCdeep)
M_ABCdist               Multi-source M-ABC with distance accumulation
M_ABCdist.WC            Multi-source M-ABC fused with WeightedClust
M_ABCpp                 Multi-source Aggregating Bundles of Clusters
                        (M-ABC), C++-accelerated
NEMO                    NEMO: neighborhood-based multi-omics clustering
Normalization           Normalisation of features
PathwayAnalysis         Pathway analysis with intersection over
                        iterations
Pathways                Pathway analysis for multiple clustering
                        results
PathwaysIter            Iterations of the pathway analysis
PathwaysSelection       Pathway analysis for a selection of objects
PlotPathways            A GO plot of a pathway analysis output.
PreparePathway          Prepare data for pathway analysis
ProfilePlot             Plotting gene profiles
ReorderToReference      Relabel and reorder clusterings to a reference
SNF                     Similarity network fusion
SelectnrClusters        Select the number of clusters via silhouette
                        widths
SharedComps             Objects shared across clusterings
SharedGenesPathsFeat    Shared genes, pathways and features across
                        methods
SharedSelection         Intersection of genes and pathways over
                        multiple methods for a selection of objects.
SharedSelectionLimma    Intersection of genes over multiple methods for
                        a selection of objects.
SharedSelectionMLP      Intersection of pathways over multiple methods
                        for a selection of objects.
SimilarityHeatmap       A heatmap of similarity values between objects
SimilarityMeasure       Similarity of a set of clusterings to a
                        reference
TrackCluster            Track a cluster or a selection of objects
                        across multiple methods
WeightedClust           Weighted clustering
Whclust                 Weighted hierarchical clustering (weighted
                        Ward)
Wkmeans                 Weighted k-means (minimises WWCSS)
WonM                    Weighting on Membership clustering
characterize_clusters   Characterise clusters with external (biological
                        / clinical) variables
cluster_agreement       Agreement between two partitions
cluster_biology_panel   Panel of cluster characterisation plots
cluster_k_sweep         Show the clustering at every k (see how it
                        deteriorates)
cluster_selection_plot
                        Justify the number of clusters (silhouette +
                        elbow / scree)
clustering_concordance
                        Compare clustering solutions to each other (no
                        ground truth needed)
compare_clusterings     Compare two clusterings derived from distance
                        matrices
compare_methods_plot    Visually compare clustering solutions with
                        ComparePlot (no ground truth)
create_data_nuggets     Create data nuggets from a data matrix
distanceheatmaps        Determine the distance in a heatmap
embedding_plot          Two-dimensional cluster visualisation (PCA /
                        UMAP / t-SNE)
f.clustABC.MultiSource
                        Consensus clustering from stacked ABC label
                        matrices
intNMF                  Integrative NMF clustering (intNMF)
mosaic_labels           Extract a flat partition from a MosaiClusteR
                        result
mosaic_sim              Simulate a multi-source data set with planted
                        clusters
mosaic_toy              Toy multi-omics example data
nugget_cluster          Data-nugget clustering
nugget_feature_weights
                        Feature weights derived from data nuggets
plot_cluster_profile    Plot one external variable by cluster
simulate_weighting_regime
                        Simulate data for evaluating feature-weighting
                        schemes (variance vs CV)
spectral_clustering     Spectral clustering of an affinity matrix
