<p dir="auto">Recently, I've been developing fast permutation tests that can make use of numpy/scipy matrix operations.</p>
<p dir="auto">So far, I have developed 3 permutation tests: a mean test, a t-test and a pearson correlation test.</p>
<p dir="auto">To get an idea for the timings, I've benchmarked the mean test to a naive implementation on a 60x60 table and  with 1000 permutations. Below are the timings</p>
<p dir="auto">Naive time [s]: 1.91008838018<br>
Numpy time [s]: 0.0174480279287</p>
<p dir="auto">If you guys are down, I can start committing these algorithms into scipy. In the near future, I plan on implementing ANOVA tests such as an F-test and a Krustal-Wallis test.</p>
<p dir="auto">Apparently these two functions do exactly the same but there are no links between them in the documentation. Is there a reason to keep the two? If so, perhaps it's worth adding to the docs.</p>
<p dir="auto"><a href="https://github.com/scipy/scipy/blob/master/scipy/linalg/decomp_lu.py#L17">https://github.com/scipy/scipy/blob/master/scipy/linalg/decomp_lu.py#L17</a></p>