Useful functions to analyze proteomic workflows including
    number of identifications, data completeness, missed cleavages,
    quantitative and retention time precision etc. Various software
    outputs are supported such as 'ProteomeDiscoverer', 'Spectronaut',
    'DIA-NN' and 'MaxQuant'.

Complements the book 
    "Using R for Modelling and Quantitative Methods in Fisheries"
    ISBN 9780367469894, published in 
    2021 by Chapman & Hall in their "Using R series". 
    There are numerous functions and data-sets that are used in the 
    book's many practical examples.

Implements bivariate and Multivariate Quantile-on-Quantile
    Granger causality tests building on the Quantile-on-Quantile
    regression framework of Sim and Zhou (2015)
    <doi:10.1016/j.jbankfin.2015.01.013> and the quantile Granger
    causality test of Troster (2018)
    <doi:10.1080/07474938.2016.1172400>. The bivariate test estimates
    the local-linear slope in the quantile regression of y_t on lagged
    x_t with lagged y_t as control, using Gaussian kernel weights, and
    tests it against zero by paired bootstrap. The multivariate
    (conditional) test additionally conditions on a set of moderators Z
    and optional x times Z interaction terms, in the spirit of Sinha,
    Ghosh, Hussain, Nguyen and Das (2023)
    <doi:10.1016/j.eneco.2023.107021>. A Sup-Wald summary across the
    quantile grid is also provided. Heatmaps and 3D surfaces default to
    the 'MATLAB' 'Parula' colour map.

Implements Multivariate Quantile-on-Quantile Regression
    (m-QQR) of Sinha, Ghosh, Hussain, Nguyen and Das (2023)
    <doi:10.1016/j.eneco.2023.107021>, extending the bivariate
    Quantile-on-Quantile regression of Sim and Zhou (2015)
    <doi:10.1016/j.jbankfin.2015.01.013> to include exogenous moderators
    and controls with optional interaction terms. For each pair of
    quantile levels (theta of the response and tau of the regressor) the
    package fits a locally-weighted quantile regression of y on the
    principal regressor x, a lagged dependent variable, moderators Z and
    the x*Z interaction terms, using Gaussian kernel weights on the
    empirical cumulative distribution function (CDF) distance. Bootstrap
    standard errors and Koenker-Machado pseudo R-squared are reported.
    Visualisations include 'MATLAB'-style 'Parula' and 'Jet' 3D
    surfaces, heatmaps and contour plots through 'plotly'.