Correlation coefficients are frequently used as a tool to interpret data. However they (and their associated p-values) are typically not reported with uncertainties. Curran 2014 described several Monte Carlo approaches to obtaining probability distributions for Spearman’s rank correlation coefficient. I have written a python implementation and extended it to include the Pearson’s correlation coefficient and a contribution from Y. Song added the Kendall rank correlation coefficient. The latter also supports data which are left and/or right censored, for both variables, following Isobe+1986. The package is available on PyPi and can be installed with
pip install pymccorrelation
The source code is available on GitHub.