cudacellr 0.4.0

• 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.

cudacellr 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..

cudacellr 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.

cudacellr 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.

cudacellr 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.