Changes in version 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.