KLLSketch::compact_level() - the
compactors.push_back() call was invalidating references to
vector elements, causing crashes with datasets larger than ~200
observations.calculate_metrics() calls - swapped
(total_good, total_bad) to correct order
(total_pos, total_neg), fixing incorrect WoE
calculations.[lower, upper] check for the
last bin to ensure boundary values are correctly assigned.enforce_bin_cutoff() - corrected iterator invalidation when
merging bins by always erasing the higher-indexed bin.k >= 2 and k < n to prevent
undefined behavior with edge cases.compactor.size() < 2 before iteration.ob_numerical_sketch() with clearer parameter descriptions
and simplified examples.special_codes parameter with
max_n_prebins for consistency with other algorithms.Siddiqi,
Navas-Palencia) in DESCRIPTION.obwoe_apply.\dontrun{} with \donttest{}
in 12 function examples.par() restoration in examples and
vignettes.inst/WORDLIST to include technical terms and
author names (MILP, Navas, Palencia) to resolve spelling notes.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.Language: en-US to DESCRIPTION
metadata.README.Rmd with detailed algorithm
descriptions, tidymodels integration examples, and
performance metrics.CODE_OF_CONDUCT.md (Contributor Covenant v2.1)
and CONTRIBUTING.md guidelines.inst/WORDLIST for spell checking.DESCRIPTION with corrected fields (Authors,
BugReports, Depends, References).cran-comments.md for submission notes.OptimalBinningWoE is a high-performance R package for optimal binning and Weight of Evidence (WoE) transformation, designed for credit scoring and predictive modeling.
Rcpp and RcppEigen for maximum
efficiency and scalability.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.step_obwoe(): A complete recipes step for
integrating optimal binning into machine learning pipelines.tune() for hyperparameter optimization of
binning parameters (algorithm, min_bins, etc.).JEDI-MWoE for handling
multi-class target variables.ob_preprocess(): Utilities for missing value handling
and outlier detection/treatment (IQR, Z-score, Grubbs).ob_gains_table(): Computation of detailed gains tables
including IV, WoE, KS, Gini, Lift, Precision, Recall, KL Divergence, and
Jensen-Shannon Divergence.plot() methods for visualizing binning results and
WoE patterns.vignette("introduction", package = "OptimalBinningWoE"))
for detailed examples and theoretical background.