a Non-Parametric Statistical Significance Test for Rolling Window
Correlation // 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.