deal with missing data flagged as NaN in observations/predictions

The general approach is to "eliminate" the time steps where observations
or predictions are missing as early as possible in the algorithm. The
best approach seemed to update the user-provided temporal masks to
also mask those time steps with missing data.

An alternative approach would have been to create a view on the
observations and predictions, e.g. using something like
`xt::view(obs, ..., xt::drop(...))`, but this produces a non-contiguous
view which cannot be sorted with `xt::sort` later to determine the
quantiles.

This is documented in `evalp` docstring and new unit tests are added.