Often what is slow in an R program is not the core analysis function you want to call, but everything you have to do before and after you call that function, IE turning your raw data into a data structure containing the relevant data to feed into the function and taking the output of the function and munging it into whatever output format you actually need. This should help speed up those bits quite a lot.

I see, thanks. I my own code I suspect that it's the for loops I can't vectorise that are making R taking so long in some operations, so this is where this project would help.

Must give it a try then.

In R its very easy to simply create a function in C++ or Fortran, then call that. Plus there's plenty of functions already that you can call instead of using for loops in R (which really aren't idiomatic).

You can use python, call out to R libraries with Rpy2 and speed up loops to faster than Julia and around native Speed with numba

I don't know about speed, but with this package you can have Haskell types at your disposal.

If you use them properly, you would not be able to compare p values of experiments directly (a common mistake).

I'm an astrophysicist, we don't really trust p-values. But it could be handy in some other areas.