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>.

A 'shiny' application for forensic kinship testing, based on
    the 'pedsuite' R packages. 'KLINK' is closely aligned with the (non-R)
    software 'Familias' and 'FamLink', but offers several unique features,
    including visualisations and automated report generation. The
    calculation of likelihood ratios supports pairs of linked markers, and
    all common mutation models.

A comprehensive set of geostatistical, visual,
    and analytical methods, in conjunction with the expanded version of the
    acclaimed J.E. Klovan's mining dataset, are included in 'klovan'. This makes the
    package an excellent learning resource for Principal Component Analysis (PCA),
    Factor Analysis (FA), kriging, and other geostatistical techniques. Originally
    published in the 1976 book 'Geological Factor Analysis', the included mining
    dataset was assembled by Professor J. E. Klovan of the University of Calgary.
    Being one of the first applications of FA in the geosciences, this dataset has
    significant historical importance. As a well-regarded and published dataset, it
    is an excellent resource for demonstrating the capabilities of PCA, FA, kriging,
    and other geostatistical techniques in geosciences. For those interested in
    these methods, the 'klovan' datasets provide a valuable and illustrative resource.
    Note that some methods require the 'RGeostats' package. Please refer to the
    README or Additional_repositories for installation instructions. This material is
    based upon research in the Materials Data Science for Stockpile Stewardship Center
    of Excellence (MDS3-COE), and supported by the Department of Energy's National
    Nuclear Security Administration under Award Number DE-NA0004104.