Alexander Plavin comments

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                                            Alexander Plavin

propagate NaN value in median

added tests: this PR makes NaN handling in median() consistent with others like mean() and sum()

Add init kwarg to mean/median to match other reductions in Base

> I think the problem is more the general awkwardness in julia of working with possibly empty collections--especially non-materialized ones--and not specific to working with missing See AccessorsExtra for a...

Add init kwarg to mean/median to match other reductions in Base

> @aplavin could you comment on this since you were a proponent of solution 5? Basically what @nalimilan says, to be able to use it with any kind of function....

Make covariance and correlation work for iterators, skipmissing in particular.

> stdlibs can also go to 2.0 before base Julia (that's the whole reason why we introduced them) So, maybe removing single-argument `cor(x)` could be done in Statistics 2.0 in...

Median (and presumably all quantile computation) could be much faster for large inputs

Can this implementation be used for `partialsort()` as well? (non-`!`) `partialsort()` returns the single element found, and doesn't modify the original array – so the observable behavior is exactly the...

point arithmetic

I'm not sure if modifying this would help with other operations, like * and /? As of now I don't see a way of scaling an array of Points (or...

skipmissing on Dict: inconsistent results

> "Fixing" this behavior is tricky Don't think it's really tricky: as I suggest in the first post, it would be great if something in `argmax(skipmissing(d))` simply threw an error...