There are quite a few algorithms to detect changepoints, outliers, mean shifts, trend shifts etc. out there. Recently I've stumbled upon BreakoutDetection and while it's new and shiny I'd like to know if there are any problems with using the algorithm to detect, specifically, multiple mean shifts in non-normally distributed random processes (e.g. studies reviewing the method).
In particular I'm worried that there are apparently two versions of the code (R and C++) and results are "sensitive to scaling", apparently showing up differently for the same underlying dataset.