StarWarsArrays.jl VS Bigsimr.jl

I raise you https://github.com/giordano/StarWarsArrays.jl

Why not Star Wars Indices (4,5,6,1,2,3,7,8,9...)? https://github.com/giordano/StarWarsArrays.jl

> The documentation disagrees about string indices not starting with 1 As priorly said, I'm speaking about strings, not `String` in particular. So, to write code which work for all AbstractString (which have basic string functions), you must not assume that the first indexing is 1, you can have degenerate cases such as : https://github.com/giordano/StarWarsArrays.jl (this is for vectors, but creating a similar type, for AbstractString isn't impossible) or just strings with an offset indexing.

There are other choices like https://github.com/simonster/TwoBasedIndexing.jl and https://github.com/giordano/StarWarsArrays.jl if you do not like 1-based indexing.

This is a total non issue as indexing is an operation that is subject to multiple dispatch. For a humorous example see https://github.com/giordano/StarWarsArrays.jl

The cool thing with Julia is that array indices aren't inherent properties, and may be changed locally by using appropriate wrappers. This means that the same underlying array may start at 0 in one part of the code, at 1 in another, and perhaps use the star-wars indexing in yet another section if that's necessary.

Adding https://github.com/giordano/StarWarsArrays.jl to the list for some extra spice

You should also check out https://github.com/giordano/StarWarsArrays.jl and https://github.com/giordano/RandomBasedArrays.jl

One more example for you. Our group wrote our core package in Julia called Bigsimr.jl (here) and then wrote interfaces to it for R (here and on cran) and Python (here and on PyPi)

Mostly, but I used it to write a package that does multivariate simulation via gaussian copulas with correlation matching. You can find it here.

We wrote a Julia package that can do this called Bigsimr which also has an R interface. Message me if you have more questions.

I wrote a Julia library that basically applies this idea, but extends it to multivariate distributions. We sample from a multivariate normal, transform the margins to uniform (via the normal cdf), and then transform to the desired distribution using the margins inverse cdf's (called the NORTA algorithm). The caveat is that this transformation is non-linear, so the correlation matrix used to generate the multivariate normal samples is generally not the same as the correlation after transformation. We account for this by numerically solving for the n*(n-1)/2 double integrals to determine what input correlation is necessary to get the desired output correlation. This paper describes the full problem and method for solving.