Changes in version 0.5.1 (2026-01-11)                  

Minor changes

  - Replaced qpmadr::solveqp() with quadprog::solve.QP() because the
    {qpmadr} package was archived on CRAN as of 2026-01-10.

                 Changes in version 0.5.0 (2025-10-05)                  

Major changes

  - Simplified specification of group penalty via abclass.control().

Minor changes

  - Changed the default alignment to lambda for cv.abclass() and refit
    in et.abclass() if a sequence of lambda's is specified. A warning
    message would be thrown out for the former.

                 Changes in version 0.4.0 (2022-09-18)                  

New features

  - Added support of sparse matrix x of class sparseMatrix (provided by
    the {Matrix} package) for abclass() and predict.abclass().
  - Added new functions named cv.abclass() and et.abclass() for training
    and tuning the angle-based classifiers with cross-validation and an
    efficient tuning procedure for lasso-type algorithms, respectively.
    See the corresponding function documentation for details.
  - Added experimental classifiers with sup-norm penalties. See the
    functions supclass() and cv.supclass() for details.

Major Changes

  - Simplified the function abclass() and moved the tuning procedure by
    cross-validation to the function cv.abclass().

Minor Changes

  - Changed the default values of the following arguments for
    abclass.control().
      - alpha: from 0.5 to 1.0
      - epsilon: from 1e-3 to 1e-4

Bug fixes

  - Fixed alignment in abclass.control().

                 Changes in version 0.3.0 (2022-05-28)                  

New features

  - Added experimental group-wise regularization by group SCAD and group
    MCP penalty.
  - Added a new function named abclass.control() to specify the control
    parameters and simplify the main function interface.

Minor changes

  - Renamed the argument max_iter to maxit for abclass().

Bug fixes

  - Fixed the validation indices in the cross-validation procedure

                 Changes in version 0.2.0 (2022-04-12)                  

New features

  - Added experimental group-wise regularization by group lasso penalty.

Minor changes

  - Removed the function call from the return of abclass() to avoid
    unnecessarily large returned objects
  - Changed the default value of lum_c for abclass() from 0 to 1.
  - Renamed the argument rel_tol to epsilon for abclass().

Bug fixes

  - Fixed the first derivatives of the boosting loss
  - Fixed the label prediction by using the fitted inner products
    instead of the probability estimates
  - Fixed the computation of regularization terms for verbose outputs in
    AbclassNet
  - Fixed the computation of validation accuracy in cross-validation
  - Fixed the assignment of lum_c in the associated header files.
  - Fixed the computation of lower bound for distinct observation
    weights

                 Changes in version 0.1.0 (2022-03-07)                  

New features

  - The first release of abclass providing the multi-category
    angle-based large-margin classifiers with various loss functions.