Topological data analytic methods in machine learning rely on
    vectorizations of the persistence diagrams that encode persistent
    homology, as surveyed by Ali &al (2000)
    <doi:10.48550/arXiv.2212.09703>.  Persistent homology can be computed
    using 'TDA' and 'ripserr' and vectorized using 'TDAvec'.  The
    Tidymodels package collection modularizes machine learning in R for
    straightforward extensibility; see Kuhn & Silge (2022,
    ISBN:978-1-4920-9644-3).  These 'recipe' steps and 'dials' tuners make
    efficient algorithms for computing and vectorizing persistence
    diagrams available for Tidymodels workflows.

A comprehensive toolset for any
    useR conducting topological data analysis, specifically via the
    calculation of persistent homology in a Vietoris-Rips complex.
    The tools this package currently provides can be conveniently split
    into three main sections: (1) calculating persistent homology; (2)
    conducting statistical inference on persistent homology calculations;
    (3) visualizing persistent homology and statistical inference.
    The published form of TDAstats can be found in Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.   
    For a general background on computing persistent homology for
    topological data analysis, see Otter et al. (2017)
    <doi:10.1140/epjds/s13688-017-0109-5>.
    To learn more about how the permutation test is used for
    nonparametric statistical inference in topological data analysis,
    read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>.
    To learn more about how TDAstats calculates persistent homology,
    you can visit the GitHub repository for Ripser, the software that
    works behind the scenes at <https://github.com/Ripser/ripser>.
    This package has been published as Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.

Provides a set of tools for managing time-series data,
    with a particular emphasis on defining various frequency types such
    as daily and weekly. It also includes functionality for converting
    data between different frequencies.