While using the Hodges Lehmann Mean in DescTools (DescTools::HodgesLehmann), I found that it generated incorrect answers (see <https://github.com/AndriSignorell/DescTools/issues/97> https://github.com/AndriSignorell/DescTools/issues/97). The error is driven by the existence of tied values forcing wilcox.test in Base R to switch to an approximate algorithm that returns incorrect results - see <https://aakinshin.net/posts/r-hodges-lehmann-problems/> https://aakinshin.net/posts/r-hodges-lehmann-problems/ for a detailed exposition of the issue.
Andri Signorell and Cyril Moser have a new C++ implementation of DescTools::HodgesLehmann using a O(N log(N)) algorithm due to Monahan, but wilcox.test in Base R appears to be still broken. Will someone kindly bring this observation, as well as the existence of a solution, to the attention of the relevant person(s) in the Base R development team? The paper by Mohanan, as well as the original Fortran implementation of the algorithm are linked to from <https://github.com/AndriSignorell/DescTools/issues/97> https://github.com/AndriSignorell/DescTools/issues/97). Inefficient O(N^2) algorithms for the Hodges-Lehmann mean are known and are implemented in a variety of packages. For example, the authors of rt.test (https://cran.r-project.org/web/packages/rt.test) use the O(N^2) approach. I suspect that Andri and Cyril will be more than happy to assist with fixing wilcox.test in Base R with their implementation of Monahan's fast algorithm. Sincerely Thomas Philips [[alternative HTML version deleted]] ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
