The Genie algorithm
    (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>) is a robust
    and outlier-resistant hierarchical clustering method
    (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>).
    This package features its faster and more powerful version.
    It allows clustering with respect to mutual reachability distances,
    enabling it to act as a noise point detector or a version of
    'HDBSCAN*' that can identify a predefined number of
    clusters. The package also features an implementation
    of the Gini and Bonferroni inequality indices, external cluster validity
    measures (e.g., the normalised clustering accuracy, the adjusted Rand index,
    the Fowlkes-Mallows index, and normalised mutual information), and internal
    cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin,
    Ball-Hall, Silhouette, and generalised Dunn indices).
    The 'Python' version of 'genieclust' is available via 'PyPI'.

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.

Provides tools to interact nicely with the 'Genius' API 
    <https://docs.genius.com/>. 
    Search hosted content, extract associated metadata and retrieve lyrics with ease.

Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.