HNPclassifier 0.2.0

Breaking changes

• hnp_umbrella() has a new interface. It now takes separate X and Y arguments together with importance_order instead of a single data frame S and a class_col. It supports an arbitrary number of ordered classes (T >= 2) rather than only ternary classification.
• hnp_summary() now takes classifier, X, Y and importance_order instead of data and class_col, and accepts classifiers that return class labels, probability matrices or score matrices, as well as fitted model objects.
• hnp_map_classes() now accepts a variable number of class labels via ... (in decreasing priority order) instead of the fixed class_1, class_2, class_3 arguments.
• Removed probability_to_score_1(), probability_to_score_2(), hnp_umbrella_flex() and hnp_box_plot().

New features

• Generalized the H-NP umbrella algorithm to any number of ordered classes (T >= 2).
• Added support for user-supplied pretrained models and pre-computed score matrices in hnp_umbrella() via the pretrained_model and input_is_score arguments.
• Added grid search over candidate thresholds (grid_search, grid_set, max_grid, max_combinations) to minimize the weighted misclassification objective, with a recursive multi-class threshold search.
• Added hnp_boxplot() to visualize and summarize under-classification and overall error from confusion matrices, supporting single- and two-method comparisons.
• Added data-generation helpers for examples and simulation (gen_data(), gen_normal_data(), generate_ball_data(), etc.) and a neural-network scoring helper (train_nn_and_get_scores()).

Improvements and bug fixes

• hnp_delta_search() now uses stats::pbinom() instead of an explicit choose()-based summation, avoiding numerical overflow and underflow for large samples.
• hnp_upper_bound() is now vectorized over the score functions and adds validation for non-finite, NA and length-mismatched score outputs.
• Added stricter input validation across the public functions (class labels, control levels, tolerances and split ratios).
• Added internal label mapping so that original class labels are preserved in predictions and summaries.