Changes in version 0.1.0 (2026-07-29)                  

First public release. MosaiClusteR is an umbrella framework for
multi-source and multi-omics clustering: it gathers a broad catalogue of
integration methods behind one consistent interface, together with the
tooling needed to preprocess, select, evaluate, compare, characterise
and biologically interpret the results.

Framework

  - Unified interface. Every method takes the same input — a List of
    numeric matrices over the same objects (objects in rows) — and
    returns the same {DistM, Clust} contract, so methods are
    interchangeable and directly comparable. Precomputed dissimilarities
    are accepted via type = "dist".
  - Four-layer workflow. Preprocess → baseline → integrate → evaluate.

Integration methods (five paradigms)

  - Direct: ADC(), ADEC().
  - Similarity-based: WeightedClust(), WonM(), SNF(), NEMO().
  - Graph-based: EnsembleClustering() (CSPA/HGPA/MCLA), HBGF(),
    ClusteringAggregation().
  - Voting / consensus: CEC(), CVAA(), ConsensusClustering(),
    EvidenceAccumulation(), LinkBasedClustering() (CTS/SRS/ASRS), and
    the Aggregating Bundles of Clusters family M_ABCpp() / M_ABCdist() /
    M_ABCdist.WC() / M_ABCdeep() (a deep-learning, autoencoder-based
    variant).
  - Hierarchy-based: HierarchicalEnsembleClustering(), EHC().
  - Factor / low-rank / spectral: intNMF(), spectral_clustering(),
    LUCID() (supervised, outcome-guided).
  - Cluster() provides the single-source baseline primitive.

Preprocessing, selection and evaluation

  - Preprocessing: Normalization() and Distance() (Euclidean, Manhattan,
    Tanimoto/Jaccard for binary data, correlation-based, and more).
  - Cluster-number selection: SelectnrClusters(), ChooseCluster().
  - Evaluation: mosaic_labels(), cluster_agreement() (ARI / NMI /
    Jaccard / purity), compare_clusterings(), CompareSilCluster(),
    CompareSvsM(), and data-driven source weighting
    (DetermineWeight_SilClust(), DetermineWeight_SimClust()).

Feature weighting and data nuggets

  - A data-nugget compression provides a robust, big-data-friendly
    feature weighting as an alternative to variance:
    create_data_nuggets(), nugget_feature_weights(), nugget_cluster(),
    Wkmeans(), Whclust().

Characterisation, visualisation and interpretation

  - Characterisation / navigation: CharacteristicFeatures(),
    FeatSelection(), FeaturesOfCluster(), FindCluster(), FindElement(),
    TrackCluster(), ReorderToReference(), SharedComps(),
    SimilarityMeasure().
  - Visualisation: ComparePlot(), ClusterPlot(), Cyclogram(),
    SimilarityHeatmap(), HeatmapPlot(), ProfilePlot(),
    ContFeaturesPlot(), BinFeaturesPlot_SingleData()/_MultipleData(),
    BoxPlotDistance(), and colour helpers (ColorPalette(),
    ColorsNames(), ClusterCols(), LabelCols()).
  - Biological interpretation (omics): DiffGenes(), PathwayAnalysis(),
    Pathways(), Geneset.intersect(), SharedGenesPathsFeat() and related
    functions connect clusters to genes and pathways.

Data and tests

  - Bundled mosaic_toy dataset and the mosaic_sim() structured-data
    generator.
  - A testthat suite covering distances, weighting schemes, the
    integration methods, evaluation metrics and data nuggets.

Notes

  - A compiled kernel (src/) accelerates the consensus step; building
    from source needs a C/C++ toolchain (Rtools on Windows). Heavier and
    specialised dependencies — SNFtool, circlize, plotrix, ggplot2,
    igraph, limma, MLP, biomaRt, org.Hs.eg.db and others — are declared
    under Suggests and loaded only when the corresponding method is
    called.
  - A few methods rely on an external graph-partitioning executable
    (e.g. METIS for EnsembleClustering(), the METIS path of EHC(), and
    LinkBasedClustering(linkBasedMethod = "asrs")) and raise an
    informative error when it is absent; pure-R alternatives (HBGF(),
    EvidenceAccumulation(), CTS/SRS) cover the same need.