Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

Contains kernel smoothing tools designed for use by historical
    dialectologists and philologists for exploring spatial and temporal patterns
    in noisy historical language data, such as that obtained from historical texts.
    The main way in which these might differ from other implementations of kernel
    smoothing is that they assume that the function (linguistic variable) being
    explored has the form of the relative frequency of a series of discrete
    possibilities (linguistic variants). This package also offers a way of exploring
    distributions in 2-dimensional space and in time with separate kernels, and
    tools for identifying appropriate bandwidths for these.

Efficient implementation of Kernel SHAP (Lundberg and Lee,
    2017, <doi:10.48550/arXiv.1705.07874>) permutation SHAP, and additive
    SHAP for model interpretability.  For Kernel SHAP and permutation
    SHAP, if the number of features is too large for exact calculations,
    the algorithms iterate until the SHAP values are sufficiently precise
    in terms of their standard errors.  The package integrates smoothly
    with meta-learning packages such as 'tidymodels', 'caret' or 'mlr3'.
    It supports multi-output models, case weights, and parallel
    computations.  Visualizations can be done using the R package
    'shapviz'.

Producing kernel estimates of the unconditional and conditional hazard
        function for right-censored data including methods of bandwidth selection.

Kernel-based machine learning methods for classification,
        regression, clustering, novelty detection, quantile regression
        and dimensionality reduction.  Among other methods 'kernlab'
        includes Support Vector Machines, Spectral Clustering, Kernel
        PCA, Gaussian Processes and a QP solver.