Plot, process, and analyze NPO files produced by
    'Nonpareil' <http://enve-omics.ce.gatech.edu/nonpareil/>.

Implements the nonparametric causality-in-quantiles test (in mean or variance),
    returning a test object with an S3 plot() method. The current implementation uses one lag
    of each series (first-order Granger causality setup). Methodology is based on 
    Balcilar, Gupta, and Pierdzioch (2016a) <doi:10.1016/j.resourpol.2016.04.004> and 
    Balcilar et al. (2016) <doi:10.1007/s11079-016-9388-x>.

Estimates and plots (as a single plot and as a heat map) the rolling window correlation coefficients between two time series and computes their statistical significance, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/> and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) <doi:10.1016/j.ecoinf.2021.101379>.

Implements the nonparametric trend test for one or several samples
    as proposed by Bathke (2009) <doi:10.1007/s00184-008-0171-x>.
    The method provides a unified framework for analyzing trends in both
    independent and dependent data samples, making it a versatile tool for
    various study designs. The package allows for the evaluation of different
    trend alternatives, including two-sided (general trend), monotonic
    increasing, and monotonic decreasing trends. As a nonparametric
    procedure, it does not require the assumption of data normality,
    offering a robust alternative to parametric tests.