A new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions.
    Song, H. and Wu, M.C. (2023) 
    <arXiv:2307.15268>.

Nonparametric kernel distribution function estimation is performed. Three bandwidth selectors are implemented: the plug-in selectors of Altman and Leger and of Polansky and Baker, and the cross-validation selector of Bowman, Hall and Prvan. The exceedance function, the mean return period and the return level are also computed. For details, see Quintela-del-Río and Estévez-Pérez (2012) <doi:10.18637/jss.v050.i08>.

An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.

Gaussian process regression with an emphasis on kernels.
    Quantitative and qualitative inputs are accepted. Some pre-defined
    kernels are available, such as radial or tensor-sum for
    quantitative inputs, and compound symmetry, low rank, group kernel
    for qualitative inputs. The user can define new kernels and
    composite kernels through a formula mechanism. Useful methods
    include parameter estimation by maximum likelihood, simulation,
    prediction and leave-one-out validation.

A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.

Smoothed bootstrap and functions for random generation from
             univariate and multivariate kernel densities. It does not
             estimate kernel densities.

Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.