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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.

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.
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