Changes in version 3.0.0 (2026-01-07) What’s new: - Fully aligned with the new XGBoost R API: deprecated watchlist removed, predictions use the standard predict(model, xgb.DMatrix) method, eliminating future-breaking warnings. - Bayesian optimization redesigned to use a pure base-R surrogate approach, removing GPfit-related numerical failures while preserving the same function signature and outputs. - Robust failure handling across optimization and modeling: failed models are safely ignored, edge cases handled explicitly, and searches always return valid results. - Reduced dependency footprint, relying mainly on base R plus xgboost and imputeTS, simplifying installation and maintenance. - Cleaner, more stable modeling core (engine, sequencer, hood) with safer cross-validation, early stopping, residual sampling, and base svd() for dimensionality reduction. - Output and API backward compatibility preserved: existing code using audrex, random_search, or bayesian_search continues to work unchanged. - Comprehensive test coverage added via testthat, validating all major paths (gbtree, gblinear, random search, Bayesian search, full audrex pipeline). Changes in version 2.0.0 (2022-03-21) - Changed the whole architecture: from one-step function to multi-point models for each sequence - Added latent dimension reduction with svd - Added automatic differentiation via recursive F-test for de-trending and removed deriv - Added Yeo-Johson normalization and removed minmax - Expanded the available statistics both in predictions and pred_stats Changes in version 1.0.1 (2021-08-18) - Added minmax normalization and removed shift feature - Expanded the available statistics both in predictions and pred_stats - Added cross-validation through expanding windows - Added two datasets - Added link to article in Rpubs Changes in version 1.0.0 (2021-04-28) - Added a NEWS.md file to track changes to the package.