abclass 0.5.1

Minor changes

• Replaced qpmadr::solveqp() with quadprog::solve.QP() because the {qpmadr} package was archived on CRAN as of 2023-01-10.

abclass 0.5.0

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.

abclass 0.4.0

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().

abclass 0.3.0

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

abclass 0.2.0

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

abclass 0.1.0

New features

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