Changes in version 0.99.7                        

  - Added RunKODAMAclustering() for matrices, KODAMA graphs,
    SingleCellExperiment, SpatialExperiment, Seurat, Giotto, and lists
    of Seurat objects.
  - Made KODAMA.clustering() a focused adapter to fastEmbedR's Louvain,
    Leiden, and Walktrap implementations, removing competing R-level
    clustering behavior from the KODAMA workflow.
  - Cluster memberships are stored in the native observation metadata of
    each supported container, while complete clustering diagnostics are
    retained in KODAMA state where available.

                       Changes in version 0.99.6                        

  - Added RunFastPCA() as the primary native float32 PCA interface for
    numeric matrices, SingleCellExperiment, SpatialExperiment, Seurat,
    and Giotto. Container methods select and transpose expression assays
    internally and store PCA scores using each framework's
    dimensional-reduction interface. The lower-level kodama_pca() and
    KODAMA.pca() names remain available for compatibility.
  - Added a session-wide options(n.cores = ...) setting, with N_CORES as
    an environment fallback, for PCA, graph construction, KODAMA
    optimization, and visualization. CPU-only spatial feature selection
    follows the same core-count setting. Explicit n.cores arguments
    retain priority.
  - Preserved sparse assays during spatial feature selection and bounded
    dense C++ work buffers by feature batches.
  - Spatial feature-selection container methods now return the updated
    object, store statistics and ranks in feature metadata, and allow
    RunFastPCA(nfeatures = ...) to consume the ranking directly.

                       Changes in version 0.99.2                        

  - Standardized session-wide backend selection across KODAMA, fastPLS,
    fastEmbedR, and faissR through options(backend = ...) and BACKEND.
    Legacy KODAMA-specific selectors remain compatibility fallbacks.

                       Changes in version 0.99.1                        

  - Established tkcaccia/KODAMA as the canonical repository for the new
    C++-backed R package and preserved the classic implementation in
    tkcaccia/KODAMAlegacy, aligning the repository and package names
    required for Bioconductor submission.
  - Replaced the legacy R implementation with bindings to the standalone
    float32 kodama-cpp library.
  - Added reusable KODAMA.graph(), KNN and PLS-LDA optimization, CPU,
    CUDA, and Metal execution, and graph-input workflows.
  - Added adapters for SingleCellExperiment, SpatialExperiment, Seurat,
    and Giotto objects.
  - Retained the historical MetRef, USA, and lymphoma datasets.
  - Renamed the former implementation repository to KODAMAlegacy; this
    package now owns the canonical KODAMA name.