Hi! There are several widely cited papers with hundreds of citations which use multivariate KS tests to test whether 2 samples are drawn from the same distribution. There are many results returned when searching for a python implementation (even asking for it in scipy), but no installable or maintained package. I was going to contribute a 2-D and N-D two-sided KS test to astropy.stats, but was told to check here on scipy, since it might fit better. There is a lot of use for it outside of astronomy. Please see my proposal in gh_14065 for details, https://github.com/astropy/astropy/issues/14065. The basic idea is to implement the seminal paper algorithm from Peacock 1983 with the computational improvement in Fasano & Franceshini 1987, both with 350+ citations. However, while I was writing the issue, I found a newly published code in R (with Rcpp) with an MIT license, that has just implemented the same in algorithms in 2021. Their paper only has 1 citation but it is new. https://github.com/nesscoder/fasano.franceschini.test With the MIT license and their benchmarked paper, does it make sense to use their implementation as a baseline, despite the fact that it wouldn't count as a significant paper currently? Also, since this is my first contribution to scipy, I am not sure of the workflow (how widely is an issue discussed before PR is looked at). I will open a PR if the suggestion makes sense and my proposal looks good, but would appreciate any thoughts on it. Kind regards, Emir Karamehmetoglu Postdoctoral Researcher Department of Physics and Astronomy Aarhus University, Denmark.