K-Medians R package Kmedians version 2.2.0 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>. Software Imports: foreach, doParallel,parallel, genieclust, Gmedian,mvtnorm, capushe, ggplot2, reshape2 Antoine Godichon-Baggioni <antoine.godichon_baggioni@upmc.fr> Comprehensive R Archive Network (CRAN) Antoine Godichon-Baggioni [aut, cre, cph], Sobihan Surendran [aut] GPL (>= 2) 2023-12-18 application/tgz https://CRAN.R-project.org/package=Kmedians doi:10.32614/CRAN.package.Kmedians