CRAN/E | NonParRolCor

NonParRolCor

a Non-Parametric Statistical Significance Test for Rolling Window Correlation

Installation

About

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) and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) doi:10.1016/j.ecoinf.2021.101379.

Key Metrics

Version 0.8.0
R ≥ 3.5.0
Published 2022-10-30 516 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks NonParRolCor results

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Maintainer

Maintainer

Josue M. Polanco-Martinez

josue.m.polanco@gmail.com

Authors

Josue M. Polanco-Martinez

aut / cph / cre

Jose L. Lopez-Martinez

ctb

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

NonParRolCor archive

Depends

R ≥ 3.5.0
gtools
pracma
colorspace
doParallel

Imports

foreach
scales