Algorithms of distance-based k-medoids clustering: simple and fast 
  k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. 
  Calculate distances for mixed variable data such as Gower, Podani, Wishart, 
  Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and 
  relative criteria. The internal criteria includes silhouette index and shadow 
  values. The relative criterium applies bootstrap procedure producing a heatmap 
  with a flexible reordering matrix algorithm such as complete, ward, or average 
  linkages. The cluster result can be plotted in a marked barplot or pca biplot.

Online, Semi-online, and Offline K-medians algorithms are
    given. For both methods, the algorithms can be initialized
    randomly or with the help of a robust hierarchical
    clustering. The number of clusters can be selected with the
    help of a penalized criterion. We provide functions to provide
    robust clustering. Function gen_K() enables to generate a sample
    of data following a contaminated Gaussian mixture.
    Functions Kmedians() and Kmeans() consists in a K-median and a
    K-means algorithms while Kplot() enables to produce graph for both
    methods. 
    Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. <doi:10.3150/11-BEJ390>.
    Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 <doi:10.1007/s11749-016-0519-x>.
    Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians"     <arXiv:2209.03597> 
    Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. <doi:10.1073/pnas.97.4.1423>.

Contains tools for rapidly computing distance matrices 
    and clustering large sequence datasets using fast alignment-free 
    k-mer counting and recursive k-means partitioning. 
    See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> 
    for a review of k-mer counting methods and applications for 
    biological sequence analysis.