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CCNR 0.2.0

• 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.

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.
Health stats visible at Monitor.