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.