pdeffebach

Works with floats. I would guess some call to `similar` that isnt correct. ``` julia> b = [missing missing; 0.0 1.0] 2×2 Matrix{Union{Missing, Float64}}: missing missing 0.0 1.0 julia> mean(b,...

Error is [this line](https://github.com/JuliaStats/Statistics.jl/blob/0588f2cf9e43f9f72af5802feaf0af4b652c3257/src/Statistics.jl#L176) ``` julia> Statistics._mean_promote(missing, 1) 1 julia> 1/1 1.0 julia> missing / 1 missing ``` The solution is probably to add more `f(x) / 1` checks to...

FWIW, R returns `NULL` which is roughly equivalent to `nothing`.

I would like to help with this effort. What does this PR need? A review? Other feedback? My understanding is that this PR is basically the union of `Statistics.jl` and...

Update, the inference failure happens because `x` is a vector which *may* contain `missing`. A simpler MWE: ``` julia> x = Union{Int, Missing}[1, 2, 3] 3-element Vector{Union{Missing, Int64}}: 1 2...

I am working on `scalarstats.jl` right now. If something is in `Statistics.jl` but could go in `scalarstats.jl`, should I move it out of `Statistics.jl` and into `scalarstats.jl`?

I have added the functionality we want and added tests. What's left, assuming what I've written is okay, is to disallow some things that only kind of work at the...

I have added many more tests. Everything is covered. The rules for ``` cov(X::Matrix, y::itr) ``` Are that the *rows* of `X` must be observations. you can't use `dims =...

I was under the impression that `collect` was a no-op for vectors. I will add those methods.

@bkamins you are right about non-allocations. But adding methods results in tons of method ambiguity errors. To resolve this without re-thinking the whole dispatch scheme, I implemented ``` _lazycollect(x::Any) =...