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.

Implementation of Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by T. Senga Kiessé and G. Durrieu (2024) <doi:10.1016/j.spl.2024.110078>.The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels.

Fast implementation of Nadaraya-Watson kernel regression for either univariate or multivariate responses, with one or more bandwidths. K-fold cross-validation is also performed.

Kernel Machine Score Test for Pathway Analysis in the Presence of 
    Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) 
    <doi: 10.1177/0962280216653427>.

Functions for kernel smoothing (and density estimation)
  corresponding to the book: 
  Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".