Nanmean for 3d array

General Usage
4

You can define your own version of missmean which checks for nans and missings and works along any dimension like this:

misssum((s,n), x) = (x===missing || isnan(x)) ? (s,n) : (s+x, n+1)
division((s,n)) = s/n
missmean(a;dims=:) = division.(reduce(misssum, a, init = (zero(eltype(a)), 0), dims=dims))

#And then call it on your data
A = [1.0:10.0 fill(missing,10)]
A[1,2] = NaN
missmean(A, dims=2)
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