Fast and Robust Hierarchical Clustering with Noise Points
Detection
A retake on the Genie algorithm
(Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>) - 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), external cluster validity measures
(e.g., the adjusted asymmetric accuracy and partition similarity scores
such as the adjusted Rand, Fowlkes-Mallows, adjusted mutual information,
and pair sets indices),
and internal cluster validity indices (e.g., the Calinski-Harabasz,
Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices).
See also the 'Python' version of 'genieclust' available on 'PyPI', which
supports sparse data, more metrics, and even larger datasets.

person(given = "Alejandro",
family = "Ochoa",
role = c("aut", "cre"),
email = "alejandro.ochoa@duke.edu",
comment = c(ORCID = "0000-0003-4928-3403"))
Implements readers and writers for file formats associated with genetics data.  Reading and writing plink BED/BIM/FAM formats is fully supported, including a lightning-fast BED reader and writer implementations.  Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles plink and eigenstrat tables (FAM, BIM, IND, and SNP files).  There are also "make" functions for FAM and BIM tables with default values to go with simulated genotype data.
testthat, knitr, rmarkdown, lfa, BEDMatrix, snpStats, pryr
Alejandro Ochoa [aut, cre] (<https://orcid.org/0000-0003-4928-3403>)