OptimalBinningWoE 1.0.3

• Critical Bug Fixes - KLL Sketch Algorithm (2026-01-20):
  • Fixed iterator invalidation in KLLSketch::compact_level() - the compactors.push_back() call was invalidating references to vector elements, causing crashes with datasets larger than ~200 observations.
  • Fixed parameter order bug in calculate_metrics() calls - swapped (total_good, total_bad) to correct order (total_pos, total_neg), fixing incorrect WoE calculations.
  • Fixed half-open interval logic in bin assignment - added explicit closed interval [lower, upper] check for the last bin to ensure boundary values are correctly assigned.
  • Fixed merge direction logic in enforce_bin_cutoff() - corrected iterator invalidation when merging bins by always erasing the higher-indexed bin.
  • Added bounds safety checks in DP optimization - ensured k >= 2 and k < n to prevent undefined behavior with edge cases.
  • Added underflow guard in compaction loop - check for compactor.size() < 2 before iteration.
  • Added input validation for non-finite values (Inf, NaN) in sketch updates.
  • Improved documentation in ob_numerical_sketch() with clearer parameter descriptions and simplified examples.
  • Replaced special_codes parameter with max_n_prebins for consistency with other algorithms.
• CRAN Reviewer Feedback (2026-01-17):
  • Removed single quotes from author names (Siddiqi, Navas-Palencia) in DESCRIPTION.
  • Removed commented-out code from examples in obwoe_apply.
  • Replaced all \dontrun{} with \donttest{} in 12 function examples.
  • Added proper par() restoration in examples and vignettes.

OptimalBinningWoE 1.0.2

• CRAN Resubmission:
  • Updated inst/WORDLIST to include technical terms and author names (MILP, Navas, Palencia) to resolve spelling notes.
  • Fixed README.md links for CONTRIBUTING.md and CODE_OF_CONDUCT.md to use absolute GitHub URLs, ensuring compliance with CRAN URI checks for ignored files.
  • Added Language: en-US to DESCRIPTION metadata.

OptimalBinningWoE 1.0.1

• CRAN Preparation: Comprehensive updates for CRAN submission compliance.
• Documentation:
  • Enhanced README.Rmd with detailed algorithm descriptions, tidymodels integration examples, and performance metrics.
  • Added CODE_OF_CONDUCT.md (Contributor Covenant v2.1) and CONTRIBUTING.md guidelines.
  • Added inst/WORDLIST for spell checking.
• Metadata:
  • Updated DESCRIPTION with corrected fields (Authors, BugReports, Depends, References).
  • Added cran-comments.md for submission notes.

OptimalBinningWoE 1.0.0

Initial Release

OptimalBinningWoE is a high-performance R package for optimal binning and Weight of Evidence (WoE) transformation, designed for credit scoring and predictive modeling.

Key Features

• Comprehensive Algorithm Suite: Implementation of 36 binning algorithms:
  • 20 Numerical Algorithms: Including MDLP (Minimum Description Length Principle), JEDI (Joint Entropy-Driven Information), MOB (Monotonic Optimal Binning), Sketch (KLL/Count-Min for large data), and more.
  • 16 Categorical Algorithms: Including ChiMerge, Fisher’s Exact Test Binning (FETB), SBLP (Similarity-Based LP), JEDI-MWoE (Multinomial WoE), and others.
• High Performance: Core algorithms are implemented in C++ using Rcpp and RcppEigen for maximum efficiency and scalability.
• Unified Interface:
  • obwoe(): Master function for optimal binning with automatic type detection and algorithm selection.
  • ob_apply_woe_num() / ob_apply_woe_cat(): Functions to apply learned binning mappings to new data.
• tidymodels Integration:
  • step_obwoe(): A complete recipes step for integrating optimal binning into machine learning pipelines.
  • Supports tune() for hyperparameter optimization of binning parameters (algorithm, min_bins, etc.).
• Multinomial Support:
  • Dedicated algorithms like JEDI-MWoE for handling multi-class target variables.
• Robust Preprocessing:
  • ob_preprocess(): Utilities for missing value handling and outlier detection/treatment (IQR, Z-score, Grubbs).
• Advanced Metrics:
  • ob_gains_table(): Computation of detailed gains tables including IV, WoE, KS, Gini, Lift, Precision, Recall, KL Divergence, and Jensen-Shannon Divergence.
• Visualization:
  • S3 plot() methods for visualizing binning results and WoE patterns.

usage

• See the package vignette (vignette("introduction", package = "OptimalBinningWoE")) for detailed examples and theoretical background.