That is, *any* distance involving a point that has a nan is nan.
This seems like a reasonable default behavior.
What should nanpdist(y) be?
Based on your code snippet on StackOverflow and your comment in the github
issue, my understanding is this: for any pair, you ignore the coordinates
where either has a nan (i.e. compute the distance in a lower dimension).
In this case, pdist(y) would be
[nan, 4, 6, 8, 2, 4, 6, 2.83, 5.66, 2.83]
(I'm not sure if you would put nan or something else in that first position.)
Or, if we use the scaling of `n/(n - p)` that you suggested in the github issue,
where n is the dimension of the observations and p is the number of "missing"
coordinates,
[nan, 8, 12, 16, 4, 8, 12, 2.83, 5.66, 2.83]
Is that correct?
What's the use-case for this behavior? How widely used is it?