In self-reported or anonymised data the user often encounters
    heaped data, i.e. data which are rounded (to a possibly different degree
    of coarseness). While this is mostly a minor problem in parametric density
    estimation the bias can be very large for non-parametric methods such as kernel
    density estimation. This package implements a partly Bayesian algorithm treating
    the true unknown values as additional parameters and estimates the rounding
    parameters to give a corrected kernel density estimate. It supports various
    standard bandwidth selection methods. Varying rounding probabilities (depending
    on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>).
    Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>),
    as well as data aggregated on areas is supported.

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.

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.