the clustering quality while retaining the single linkage speed,

Title: Fast, Robust, and Outlier Resistant Hierarchical Clustering
Description: Includes the reference implementation of Genie - a hierarchical
clustering algorithm that links two point groups in such a way that
an inequity measure (namely, the Gini index) of the cluster sizes
does not significantly increase above a given threshold.
This method most often outperforms many other data segmentation approaches
in terms of clustering quality as tested on a wide range of benchmark
datasets. At the same time, Genie retains the high speed of the single
linkage approach, therefore it is also suitable for analysing larger data sets.
For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>).
For an even faster and more feature-rich implementation, including,
amongst others, noise point detection, see the 'genieclust' package.
Depends: R (>= 3.3.0), stats, genieclust
Author: Marek Gagolewski [aut, cre, cph]

Title: Project GENIE BioPharma Collaborative Data Processing Pipeline
Description: The American Association Research (AACR) Project
Genomics Evidence Neoplasia Information Exchange (GENIE) BioPharma
Collaborative represents a multi-year, multi-institution effort
to build a pan-cancer repository of linked clinico-genomic data.
The genomic and clinical data are provided in multiple releases (separate
releases for each cancer cohort with updates following data corrections),
which are stored on the data sharing platform
The 'genieBPC' package provides a seamless way to obtain the
data corresponding to each release from 'Synapse' and to prepare
jsonlite, purrr (>= 0.3.4), rlang (>= 1.0.0), stringr (>=
1.4.0), sunburstR, tibble (>= 3.1.2), tidyr
Author: Jessica A. Lavery [aut, cre] (<https://orcid.org/0000-0002-2746-5647>),
Maintainer: Jessica A. Lavery <laveryj@mskcc.org>

Title: The Genie++ Hierarchical Clustering Algorithm with Noise Points
person("Maciej", "Bartoszuk", role = c("ctb")),
person("Anna", "Cena", role = c("ctb")),
person("Peter M.", "Larsen", role = c("ctb"))
Description: A retake on the Genie algorithm - a robust
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
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.