The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

T1FF 0.1.0

• Initial public development release.
• Added Type-1 Fuzzy Function models for binary classification and numeric regression using fuzzy C-means and membership-weighted local predictions.
• Added formula and column interfaces, categorical predictor encoding, feature scaling, and explicit missing-value handling.
• Added automatic separation detection, dependency-free ridge-logistic fallback, and configurable probability clipping.
• Added validation, K-fold, and stratified K-fold hyperparameter tuning with task-appropriate metrics.
• Added MAPE and SMAPE regression metrics to tuning, evaluation, and nested benchmarking, with explicit handling of zero actual values.
• Added efficient joint tuning of cluster count, fuzziness, and classification threshold for threshold-dependent metrics.
• Added model evaluation and repeated nested cross-validation with matched logistic or linear regression baselines.
• Added optional probabilistic SVM and epsilon-SVR learners within fuzzy clusters via local_model = "svm".
• Tightened public-input validation for integer resampling controls, logical scaling flags, classification labels, and validation split sizes.
• Ensured unresolved final-model GLM instability warnings remain visible after tuning while routine candidate-fit warnings stay contained.
• Added a comprehensive vignette and PDF reference manual.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.