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 
    'Synapse' <https://www.synapse.org/>. 
    The 'genieBPC' package provides a seamless way to obtain the
    data corresponding to each release from 'Synapse' and to prepare
    datasets for analysis.

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 inequality indices
    (the Gini, Bonferroni index), external cluster validity measures
    (e.g., the normalised clustering accuracy and partition similarity scores
    such as the adjusted Rand, Fowlkes-Mallows, adjusted mutual information,
    and the pair sets index),
    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.

Implements readers and writers for file formats associated with genetics data.  Reading and writing Plink BED/BIM/FAM and GCTA binary GRM 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.