slowkow/harmonypy: v2.0.0

Complete rewrite with C++ backend (Armadillo + nanobind), matching the R harmony2 package step-by-step.

Highlights

• ~10x faster than v0.1.0: 858k cells in ~36s on Apple M1 Ultra (vs ~340s previously)
• Matches R harmony2: correlation ≥0.998 across all PCs
• Minimal dependencies: only numpy at runtime
• Pre-built wheels for Linux (x86_64, aarch64) and macOS (x86_64, arm64), Python 3.9–3.13

New

• C++ backend with BLAS-accelerated dense matrix ops (Accelerate on macOS, OpenBLAS on Linux)
• Custom scatter/gather kernels replace all sparse matrix operations
• K-means initialization matches R exactly (Gumbel-max cosine-distance sampling)
• ncores parameter to control BLAS thread count
• batch_prop_cutoff parameter for underrepresented batch handling
• Arrowhead matrix inverse for fast single-covariate correction
• Accepts pandas DataFrame, dict of arrays, or NumPy array for meta_data
• C++ progress messages routed through Python logging for proper integration with downstream packages (thanks @yakirr)

Breaking changes

• lamb defaults to automatic estimation (was fixed 1). Pass lamb=1 for old behavior.
• Default parameters changed to match R harmony2: max_iter_kmeans 20→4, epsilon_cluster 1e-5→1e-3, epsilon_harmony 1e-4→1e-2
• Only numpy required at runtime (previously pandas, scipy, scikit-learn)

See CHANGELOG.md for full details.