Release v6.6.0

New features

• Added flexible hyperparameter tuning with configurable tuning metrics and support for grid, random, and custom search strategies (#618).
• Standardized the non-Cyclops modeling interface to simplify tuning and maintenance of classification models (#618).
• Added ridge logistic regression settings via Cyclops with setRidgeRegression() (#621).
• Expanded imputation support and hardened the missing-indicator and predictive mean matching workflow (#622).
• Added support for using logits / linear predictors in rank-based metrics (#615).
• Persisted hyperparameter settings and model names in the results data model to improve downstream model identification and viewing (#633, #632).

Bug fixes

• Improved upload of hyperparameter metadata and robustness of model settings persistence for database viewers and downstream tools (#628, #623).
• Ensured existing GLM and scikit-learn model settings retain model identity so uploads generate distinct model design records (#614).
• Fixed evaluation when outcomes are single-class (#624).
• Improved LightGBM model persistence using a more robust in-memory serialization path (#626).
• Removed deprecated sklearn AdaBoost usage for compatibility with newer scikit-learn versions (#627).
• Fixed serialization of simpleImputer metadata when saving PLP models (#630).
• Limited batchRestrict handling to SQLite-backed data to avoid incorrect behavior on other backends (#612).

Performance and maintenance

• Improved simpleImpute performance for large feature sets (#629).
• Reduced GitHub Actions R CMD check runtime in CI (#625).