Implementations several algorithms for kernel k-means. The default
  'OTQT' algorithm is a fast alternative to standard implementations of kernel
  k-means, particularly in cases with many clusters. For a small number of
  clusters, the implemented 'MacQueen' method typically performs the fastest. For
  more details and performance evaluations, see Berlinski and Maitra (2025) <doi:10.1002/sam.70032>.

Weighted k-Nearest Neighbors for Classification, Regression
    and Clustering.

A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.

Miscellaneous functions for classification and visualization,
     e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, 
     an interface to 'svmlight' and stepclass() wrapper variable selection 
     for supervised classification, partimat() visualization of classification rules 
         and shardsplot() of cluster results as well as kmodes() clustering for categorical data, 
     corclust() variable clustering, variable extraction from different variable clustering models 
         and weight of evidence preprocessing.

Functions to search, retrieve, apply and update classification 
  standards and code lists using Statistics Norway's API 
  <https://www.ssb.no/klass> from the system 'KLASS'. Retrieves classifications 
  by date with options to choose language, hierarchical level and formatting.

A set of functions designed to quickly generate results of a multiple choice
          test. Generates detailed global results, lists for anonymous feedback and
          personalised result feedback (in LaTeX and/or PDF format), as well as item
          statistics like Cronbach's alpha or disciminatory power. 'klausuR' also
          includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for
          its basic features. The respective R package 'rkward' cannot be installed
          directly from a repository, as it is a part of RKWard. To make full use of this
          feature, please install RKWard from <https://rkward.kde.org> (plugins are
          detected automatically). Due to some restrictions on CRAN, the full package
          sources are only available from the project homepage.