NEWS

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CCNR 0.2.0 (2026-11-04)

• Initial release.
• Fits a Classifier Chain Network (CCN) for multi-label binary classification.
• Supports k-fold cross-validation with grid search over q, lambda, and candidate label orders.
• The order in which labels are chained can be given as a permutation, or as a function of the training data that is evaluated at the start of every fit.
• conditional_entropy_matrix() returns the conditional entropy of every label given every other label, and entropy_label_order() turns those entropies into a chain order, with methods "cebcc1" to "cebcc4" and "ebcc" following Jun et al. (2019).
• Standard methods for fitted models (predict, coef, fitted, residuals, print, summary) and for cross-validation results (predict, coef, fitted, print, summary).
• Built-in scoring functions: Hamming accuracy, exact match ratio, log-likelihood, micro-F1, macro-F1, and mean AUC.
• BFGS optimizer with Wolfe line search and optional random restarts.
• amphibians: presence of seven amphibian species at 189 Polish water reservoirs, with the site characteristics reduced to 25 principal components. Provides a multilabel data set with rare labels for examples and illustrations.