Alternatives to the Pearson correlation with p-values for Matrix
This package calculate the correlations between two variables or matrices or dataframes, for four independence coefficients; Pearson correlation, Maximum Information Coefficient (MIC), Random dependency coefficient (RDC) and Correlation of distances (dCor). These coefficients differ from the Pearson correlation in that they detect non-linear dependencies and are not subject to the normality of the data. Together with the correlations, calculate the p-values and the adjusted p-values for each type of coefficient. When the function is used for matrices and dataframes, the difference between the calculation of paired data (must have the same dimensions) or independent (they must only coincide in the same number of columns).
It consists of four functions:
AlterCorr that does the calculations for vectors of variables.
AlterCorrM that performs calculations for value matrices using the AlterCorr function.
The MIC function calculates the maximum information coefficient and its p-values with a permutations test.
The RDC function calculates the coefficient of random dependence, together with the significance test using the Bartlett approximation.