An API wrapper for 'Cryptowatch' to get prices and other information (e.g., volume, trades, order books, bid and ask prices, live quotes, and more) about cryptocurrencies and crypto exchanges. See <https://docs.cryptowat.ch/rest-api> for a detailed documentation.

Performs cryptographic randomness tests on a sequence of random
    integers or bits. Included tests are greatest common divisor, birthday spacings,
    book stack, adaptive chi-square, topological binary, and three random walk
    tests (Ryabko and Monarev, 2005) <doi:10.1016/j.jspi.2004.02.010>. Tests except greatest common divisor and birthday spacings are not
    covered by standard test suites. In addition to the chi-square goodness-of-fit
    test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are
    also generated by some of the cryptographic randomness tests.

Provides a suite of functions to parse Crystallographic Information
    Files (.cif), extracting essential data such as chemical formulas, unit cell
    parameters, atomic coordinates, and symmetry operations. It also includes
    tools to calculate interatomic distances, identify bonded pairs using various
    algorithms (minimum_distance, brunner_nn_reciprocal, econ_nn, crystal_nn), determine nearest
    neighbor counts, and calculate bond angles. The package is designed to
    facilitate the preparation of crystallographic data for further analysis,
    including machine learning applications in materials science.
    Methods are described in:
    Brunner (1977) <doi:10.1107/S0567739477000461>;
    Hoppe (1979) <doi:10.1524/zkri.1979.150.14.23>;
    O'Keeffe (1979) <doi:10.1107/S0567739479001765>;
    Shannon (1976) <doi:10.1107/S0567739476001551>;
    Pan et al. (2021) <doi:10.1021/acs.inorgchem.0c02996>;
    Pauling (1960, ISBN:978-0801403330).

Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. 'csa' package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.