NEWS

scDonorAudit 0.99.9

Correct development provenance to the maintainer-confirmed assistance scope.
Clarify strict material-reversal thresholds, equality boundaries and NULL summaries; add focused regression coverage.
Document table keys and distinguish audit issues from fitted event records.
Complete the source build, installation and evaluated-tutorial instructions.
Public APIs, numerical analysis, output schemas and example data are unchanged.

Document fixed backend settings, filtering defaults, design-audit components and the quantities shown by each plot.
Add a small single-cell aggregation example and clarify example-data metadata.
Centralize development provenance with a package-level source pointer.

Organize the README around installation, a minimal workflow and result interpretation; consolidate related-tool comparisons in the tutorial.
Place statistical and design limits alongside the results and inputs they explain, reducing repeated disclaimers while preserving their meaning.
Clarify package help and example-data documentation; public interfaces, executable analysis code and output schemas are unchanged.

Validate sparse and diagonal counts through public Matrix methods, retaining count error codes and sparse storage; add storage-class regression coverage.
Clarify two-condition design limits, cell aggregation and coverage semantics, and the distinction between current source and future Bioconductor installation.
Simplify development provenance and align source attribution pointers.
Make the output dictionary title version-independent and keep biocViews on one line; public APIs and output schemas are unchanged.

Reject logical and pattern Matrix counts with an INVALID_COUNTS error, before accessing numeric slots. Numeric sparse counts stay sparse.
Return the same 16 typed summary columns when no genes are retained.
Split design planning and fit execution into internal helpers while keeping the public interfaces, fixed filtering family and numerical backend intact.
Add package help, a complete output/status dictionary, synthetic data provenance, and a runnable mixed coverage/design-failure example.
Clarify the in-memory count requirement and preaggregated entry point, method references, functional positioning and AI-assisted development.

Revalidate prepared counts and gene IDs at both public model entries. Invalid edits fail before auditing or fitting; valid count edits, row subsets, and column reordering remain supported.
Use sample IDs for coverage plot positions, keeping donor/sample display labels separate so distinct samples cannot overlap due to label collisions.
Add dense and sparse prepared-object mutation regression tests.

Reject reserved covariate names before they can overwrite model columns.
Require syntactic covariate names and reject the special formula term ..
Use unique run IDs in effect plots, including for donors named baseline, and preserve positions for unavailable effects.
Add regression tests for both public model entries and plotting boundaries.

Added the maintainer ORCID and aligned the minimum testthat version with the stable mocking interface used by the tests.
Excluded data-raw from source builds while retaining its tracked files.
Added a clear vignette error when the Crowell19 selection rule yields fewer than two genes, and removed an unsupported fixed effect-direction claim.

Preserved backend warnings when the same donor refit later fails, and kept each deletion's original design diagnosis when the baseline is unavailable.
Added sample-aligned fit records and package/contrast provenance, plus consistency checks for pseudobulk objects whose columns or audit metadata were modified.
Clarified the output dictionary, related work, missing effects, observed ranges, and fixed-versus-re-estimated analysis choices in the vignette.

Fixed factor contrast coding so user R settings cannot reverse or rescale the named condition effect; added regression coverage for full and donor-deletion fits.
Expanded the Crowell19 vignette with a reproducible interpretation of per-donor effect changes and their limitations.
Added donor-aware pseudobulk preparation, design audits, leave-one-donor-out edgeR quasi-likelihood fits, summaries, and plots for two-condition studies.
Added explicit handling of incomplete pairs, missing cell types, low cell counts, unavailable fits, and genes that become all-zero after deletion.
Added simulated and Crowell19 Astrocyte pseudobulk walkthroughs, with runnable examples for the exported APIs.
Added Bioconductor installation guidance and a structured vignette.