Changes in version 0.4.0 (2026-10-27)                  

  - Normalize very small positive libraries without overflowing an
    intermediate scale factor, and reject non-finite library totals.

  - Reject component and feature counts beyond the supported integer
    range with an informative error before integer conversion.

  - Replaces the former cudatensr, cudasparsr, and cudalearnr
    dependencies with the unified cudaverse package.

  - Preserves all existing single-cell workflow and adapter entry
    points.

  - Keeps cuda_provenance() as a re-export of the canonical cudaverse
    generic.

                        Changes in version 0.3.0                        

  - Added cudacell_seurat() for a native Seurat v5 workflow using only
    the optional SeuratObject (>= 5.0.0) package. It writes normalized
    expression and feature statistics to an Assay5, PCA results to a
    DimReduc, exact kNN results to a Neighbor, size factors to cell
    metadata, and parameters and compute provenance to a deterministic
    tool record.
  - Seurat output names and keys are checked for cross-type collisions
    before device selection, layer realization, or computation. Existing
    object state is preserved, and overwrite = TRUE replaces only
    explicitly named cudacellr outputs.
  - Non-memory-backed Seurat layers are never realized silently. realize
    = TRUE explicitly opts into an in-memory sparse realization and
    records the original class and materialization decision.
  - Added cudacell_sce() for a native, non-destructive
    SingleCellExperiment workflow. Normalized expression, PCA, HVG
    statistics, kNN relationships, and compute provenance are written to
    the corresponding assay, reduced dimension, rowData, colPair, and
    metadata locations.
  - Existing assays, row and column metadata, reduced dimensions,
    alternative experiments, pairings, canonical size factors, labels,
    and user metadata are preserved. Output collisions fail before
    computation unless explicitly replaced with overwrite = TRUE.
  - Delayed assays are never materialized silently. realize = TRUE is
    required to opt into an in-memory sparse realization.
  - cuda_provenance() now dispatches through the shared cudaverse S3
    generic and reads the ordered computation record directly from
    SingleCellExperiment and Seurat objects produced by cudacellr.
  - cuda_cell_pca() and cudacell_workflow() now expose normalization
    scale_factor, log1p, and min_mean controls; the complete workflow
    also exposes PCA scaling through scale..

                        Changes in version 0.2.0                        

  - Normalization, HVG selection, PCA, and kNN now compose into one
    ordered cudaverse-stage/1 provenance record, including upstream
    sparse materialization.
  - cudacell_workflow() reports its aggregate CPU/CUDA/hybrid compute
    device, and its print method no longer implies that the PCA device
    represents the complete workflow.
  - Strict CUDA requests are validated before CPU preprocessing begins,
    avoiding expensive work before an unavailable-device error.
  - Re-exported cuda_provenance() as the common inspector for
    single-cell results.

                        Changes in version 0.1.2                        

  - Feature and cell identifiers now survive normalization, HVG
    selection, PCA, kNN, and the complete workflow. HVG results retain
    original feature indices, including when feature names are
    duplicated.
  - Added a concise print() method for workflow results and strict
    validation of the log1p flag.

                        Changes in version 0.1.1                        

  - cuda_cell_neighbors() and cudacell_workflow() now expose batch_size,
    using the bounded-memory exact kNN implementation in cudalearnr.
  - cudacell_workflow() now reuses its normalized matrix and
    variable-feature result for PCA instead of performing both
    preprocessing stages twice.