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Biweight midcorrelation has been implemented in the R statistical programming language as the function bicor as part of the WGCNA package [3] Also implemented in the Raku programming language as the function bi_cor_coef as part of the Statistics module. [4]
Tjøstheim's coefficient is implemented as cor.spatial in the R package SpatialPack. [4] Numerical simulations suggest that A {\displaystyle A} is an effective measure of correlation between variables but is sensitive to the degree of autocorrelation in F {\displaystyle F} and G {\displaystyle G} .
The WGCNA R software package [28] provides functions for carrying out all aspects of weighted network analysis (module construction, hub gene selection, module preservation statistics, differential network analysis, network statistics).
Example scatterplots of various datasets with various correlation coefficients. The most familiar measure of dependence between two quantities is the Pearson product-moment correlation coefficient (PPMCC), or "Pearson's correlation coefficient", commonly called simply "the correlation coefficient".
R's statistics base-package implements the correlation coefficient with cor(x, y), or (with the P value also) with cor.test(x, y). The SciPy Python library via pearsonr(x, y) . The Pandas and Polars Python libraries implement the Pearson correlation coefficient calculation as the default option for the methods pandas.DataFrame.corr and polars ...
It is possible to calculate the cophenetic correlation in R using the dendextend R package. [5] In Python, the SciPy package also has an implementation. [6] In MATLAB, the Statistic and Machine Learning toolbox contains an implementation. [7]
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Then the definition of the autocorrelation function between times and is [1]: p.388 [2]: p.165 (,) = [¯] where is the ...