GNU R Genie++ Hierarchical Clustering Algorithm with Noise Points Detection

 A retake on the Genie algorithm - a robust hierarchical clustering
 method (Gagolewski, Bartoszuk, Cena, 2016
 <DOI:10.1016/j.ins.2016.05.003>). Now faster and more memory efficient;
 determining the whole hierarchy for datasets of 10M points in low
 dimensional Euclidean spaces or 100K points in high-dimensional ones
 takes only 1-2 minutes. Allows clustering with respect to mutual
 reachability distances so that it can act as a noise point detector or a
 robustified version of 'HDBSCAN*' (that is able to detect a predefined
 number of clusters and hence it does not dependent on the somewhat
 fragile 'eps' parameter).
 .
 The package also features an implementation of economic inequity indices
 (the Gini, Bonferroni index) and external cluster validity measures
 (partition similarity scores; e.g., the adjusted Rand, Fowlkes-Mallows,
 adjusted mutual information, pair sets index).
 .
 See also the 'Python' version of 'genieclust' available on 'PyPI', which
 supports sparse data, more metrics, and even larger datasets.