imbalanceKit 0.1.0

First release. imbalanceKit collects the resampling methods of the former SMOTEWB package (version 1.2.5) and extends them.

New methods

• BiSMOTE(): bi-directional SMOTE (Sağlam & Yamak).

• MCSMOTE(): transition matrix driven oversampling (Sağlam & Cengiz, 2025). Works with more than two classes.

• UGO(): uncertainty guided oversampling (Sağlam & Cengiz, 2026), with "RWO", "SMOTE", "ROS" and "MCSMOTE" as base methods. UGO is not a resampler by itself but a procedure that uses a base method and a classifier. The classifier is chosen with the classifier argument: one of the seven built-in classifiers ("CART", "RF", "LogReg", "KNN", "NaiveBayes", "LDA", "NNET") or any function that returns class probabilities. The old names "rpart" and "randomForest" still work.

• DatRel() (Sağlam, 2025) is a post-processing method: DatRel(x, y, x_syn, y_syn) moves synthetic samples from any source that fall outside their class region back into it (pure and proper class cover; works with more than two classes). The cover algorithm of the rcccd package is included, so rcccd is not needed. oversampleDatRel() generates the samples with a base method first.

• EBSMOTE(), GQEO(), LEAO() and SyMProD(): further oversamplers that need no deep learning libraries. The first, third and fourth are defined for two classes; with more classes every class is oversampled against the union of the others. LEAO() estimates class probabilities with bagged rpart trees, logistic regression, k-nearest neighbours or naive Bayes (prob_type).

Documentation

• The year of publication of every method is part of its help page title and is listed in the README.
• References were added for EBSMOTE(), GQEO(), LEAO() and SyMProD() (also in citation()).

Metrics

• evaluate() calculates ACC, BACC, F1, GMEAN, MCC, ROCAUC, PRAUC, Brier, PBrier (penalized), BBrier (balanced), ECE, BECE (balanced), P4 and IAM plus PRECISION, RECALL, SPECIFICITY, KAPPA and LOGLOSS, or the selected ones. Each metric is also a function of its own.
• Metrics are defined for two or more classes; class-wise metrics have an average argument ("binary", "macro", "micro", "weighted").

Methods carried over from SMOTEWB 1.2.5

ADASYN(), BLSMOTE(), GSMOTE(), ROS(), ROSE(), RSLSMOTE(), RUS(), RWO(), SLSMOTE(), SMOTE() and SMOTEWB().

Changes compared with SMOTEWB 1.2.5

• Every resampler is built from a generate_<METHOD>() function (one class at a time) and a shared driver. Generation is vectorised, so the methods are several times faster on large data. Because the random numbers are now drawn in bulk, a seed no longer reproduces the output of SMOTEWB 1.2.5 (ROS() and ROSE() still do); the distribution of the synthetic samples is unchanged.
• All resamplers work with more than two classes.
• ovRate: the number of synthetic samples is ovRate * (n_max - n_j) rounded half up for every method (before: round() for most, ceiling() for BiSMOTE and UGO). ovRate may also be a vector with one value per class and is validated.
• RUS() keeps the original row order and validates n_neededToRemove.

Fixes

• SMOTE(k = 1) and ADASYN(k = 1) no longer fail; k larger than the class size is reduced. Classes with a single sample are duplicated with a warning.
• ADASYN() returns exactly the required number of samples (it was off by a few because of rounding) and its C is reported for every class.
• RSLSMOTE() used the neighbours of the first sample instead of those of the current sample when relocating; fixed.
• BLSMOTE() no longer modifies k2 across classes and no longer loops forever when too few borderline samples exist.
• GSMOTE() now returns y_syn as documented.