Changelog

Full release notes live on GitHub. The highlights below summarise major updates.

v0.3.0

  • Introduces a CpG-centric pipeline built on the validated VMR workflow, enabling million-scale panels and curated signatures with checkpointable, restart-friendly execution.

  • Heritability (h²) reporting is more robust via null calibration and an unscaling fix applied to window metrics and summaries.

  • Documentation refresh: installation notes, tuned workflow reflected across the README and tutorials, plus a new CpG pipeline user-guide page.

  • Breaking: the legacy pipeline module and its documentation references were removed; migrate to the CpG pipeline and tuned VMR workflow.

v0.2.0

  • Multi-GPU orchestration now auto-tunes max_in_flight based on the number of detected devices and keeps Dask clusters alive until every future is drained. This prevents premature shutdowns on longer studies and improves throughput on 4+ GPU systems.

  • Added cohort-wide helpers in genboostgpu.tuning, including select_tuning_windows() for stratified sampling and global_tune_params() for Optuna-backed ridge refits derived from sparsity targets.

  • Documentation now covers the reproducibility checklist, deterministic Optuna configuration, and richer tutorials linked from examples/ so new users can mirror the exact benchmarking pipelines.

v0.1.0

  • Initial public release with elastic net boosting, cis-window preprocessing, and PLINK/CuPy data loaders.