Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.

Computational tools to represent phylogenetic signals using adapted
  eigenvector maps.

Provides a curated multi-country collection of monetary
    policy shock and stance series from the empirical macroeconomics
    literature, bundled as tidy data frames with provenance metadata.
    Version 0.1.0 includes thirteen series covering the United States,
    United Kingdom, and Australia: for the US, the policy news shock of
    Nakamura and Steinsson (2018) <doi:10.1093/qje/qjy004>, the
    orthogonalised surprise of Bauer and Swanson (2023)
    <doi:10.1257/aer.20201220>, the target and path factors of the
    Swanson (2021) <doi:10.1016/j.jmoneco.2020.09.003> extension of
    Gurkaynak, Sack, and Swanson (2005), the pure monetary policy and
    central bank information shocks of Jarocinski and Karadi (2020)
    <doi:10.1257/mac.20180090>, the informationally-robust shock of
    Miranda-Agrippino and Ricco (2021) <doi:10.1257/mac.20180124>, and
    the shadow federal funds rate of Wu and Xia (2016)
    <doi:10.1111/jmcb.12300>; for the UK, the UK Monetary Policy
    Event-Study Database of Braun, Miranda-Agrippino, and Saha (2025)
    <doi:10.1016/j.jmoneco.2024.103645>, the high-frequency surprise of
    Cesa-Bianchi, Thwaites, and Vicondoa (2020)
    <doi:10.1016/j.euroecorev.2020.103375>, and the narrative shock of
    Cloyne and Hurtgen (2016) <doi:10.1257/mac.20150093>; for Australia,
    the three-component RBA surprise of Hambur and Haque (2023)
    <doi:10.1111/1475-4932.12786> and the credit-spread-augmented RBA
    narrative shock of Beckers (2020). Helpers support date alignment,
    frequency conversion, and shock cumulation. All data is bundled;
    no runtime network access is required.