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Multidimensional grid interpolation in arbitrary dimensions

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TagBot is not working. It appears to be a security issue, but I don't have time to track it down or fix it right now.

It seems that pre-specifying the vector size using `MVector` from `StaticArrays` helps to significantly improve the performance of `ind2x` ![Screenshot from 2020-03-05 23-57-18](https://user-images.githubusercontent.com/53627988/76033769-e9270800-5ef1-11ea-991e-6bad4c0ee539.png)

I tried to create a `RectangleGrid(t,p)`, where t is a Date, p is a float with no luck, I got error "ERROR: MethodError: Cannot `convert` an object of type Date...

When I pass a 2d array with complex numbers to `interpolate`, the code fails. I tries to convert the complex numbers to `Float64`.

While I can interpolate complex valued functions, e.g., `grid = RectangleGrid([0., 1., 2.])` `gridData = [1.0im, 2.0im, 3.0im]` the interpolated function returns complex conjugated values: E.g., `interpolate(grid,gridData, [0.0])` gives `0.0-1.0im`...

To be consistent with the julia style guide https://docs.julialang.org/en/v1/manual/style-guide/index.html#Write-functions-with-argument-ordering-similar-to-Julia-Base-1, we should have ```julia ind2x!(x, grid, ind) ``` rather than ```julia ind2x!(grid, ind, x) ``` This should not be a terrible...

It would be nice to have something like this: ``` julia type NearestGrid

Simplex interpolation runs surprisingly slowly in benchmarks from December 2016. See images below. Simplex is blue; multilinear interpolation is red. Simplex interpolation is always slower than multilinear interpolation for up...

help wanted

It would be nice to avoid allocations altogether at interpolation time (see also #50). This problem can alternatively be stated as "where should the memory for interpolants live?" I see...

This PR adds some allocation tests. Unfortunately they were failig.