Thanks for this cool lib! Facing a problem currently of finding the best compact set of distributions to try on data of unknown nature, given a limited time/CPU budget. As it appears, many of the distributions are subsets of each other, and result in a really twin-like behavior. When compute budget is limited, it probably has no sense to check distributions that can easily give similar shapes, it would be more reasonable to try the most diverse ones first (on average). Then, out of 2 distributions with similar avg diversity, it's better to start with the one having lower average runtime. get_common_distributions() seems to not account for diversity and avg runtime. Are you interested in research or PR resulting in a new function like get_efficient_distributions(n:int=3) that returns n most diverse and fast-to-compute distributions, on average?