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.