A comprehensive toolbox for wavelet-domain quantile
    analyses of bivariate and multivariate time series. Provides Wavelet
    Quantile Regression and Multivariate Wavelet Quantile Regression
    after Adebayo and Ozkan (2024)
    <doi:10.1016/j.jclepro.2024.140832>, Wavelet
    Quantile-on-Quantile regression with bootstrap p-values extending
    Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>, the
    nonparametric Causality-in-Quantiles test of Balcilar, Gupta and
    Pierdzioch (2016) <doi:10.1016/j.resourpol.2016.04.004> together
    with its wavelet variant, Wavelet Quantile Mediation and
    Moderation, Wavelet Quantile Correlation, and a wavelet-based
    nonparametric Quantile Density estimator. The Maximal Overlap
    Discrete Wavelet Transform (MODWT) decomposition is performed via
    'waveslim' and Short / Medium / Long band aggregation is supported
    throughout. For plain Quantile-on-Quantile regression see the
    companion CRAN package 'QuantileOnQuantile'. All interactive 3D
    surfaces, heatmaps and contour plots default to the 'MATLAB'
    'Parula' colour map.

Implements a permutation test method for the weighted quantile sum (WQS) regression, building off the 'gWQS' package (Renzetti et al. <https://CRAN.R-project.org/package=gWQS>). Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. 2015 <doi:10.1007/s13253-014-0180-3>). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. 2022 <doi:10.1289/EHP10570>).

Assess Water Quality Trends for Long-Term Monitoring Data in Estuaries using
  Generalized Additive Models following Wood (2017) <doi:10.1201/9781315370279> and Error
  Propagation with Mixed-Effects Meta-Analysis following Sera et al. (2019) <doi:10.1002/sim.8362>.
  Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and
  visualization of results.