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.

Estimation algorithms for Kullback-Leibler divergence between two probability
    distributions, based on one or two samples, and including uncertainty quantification.
    Distributions can be uni- or multivariate and continuous, discrete or mixed.

Most importantly, calculates Kullback-Leibler Divergence (KLD), Turing's perspective estimator
 and their confidence intervals.

Provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) <doi:10.1088/1742-6596/1955/1/012047>.

Kernel Learning Integrative Clustering (KLIC) is an algorithm that allows to combine multiple kernels, each representing a different measure of the similarity between a set of observations. The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.