[HACK] Porting LFI Techniques to Julia #35
Description
Porting LFI Techniques to Julia
The goal is to take one or more LFI techniques implemented in a more commonly used language (e.g. Python) and directly implement it in the Julia language (i.e. without resorting to PyCall if possible).
Contacts: Danley Hsu
Participants:
Goals and deliverable
The goal is to port at least one LFI technique to Julia and release it as a public repository on Github.
Resources needed
- Some familiarity with Julia or general high-level programming concepts
- Deep familiarity with one or more LFI techniques.
Detailed description
Julia is a new programming language which attempts to take the "readability" of Python and combine it with the computational speed of languages such as C++/Fortran. As a relatively new language, statistical packages are still being developed for the language. In particular, LFI techniques are (as far as I know) practically non-existent except for a few ABC packages. I'd like to take at least one of the techniques discussed during this workshop and implement it in Julia. Not only will this provide more tools for the Julia community, but the potential boost in calculation speed could be of benefit for those who find that their existing code (e.g. in Python) runs more slowly than they'd like. In particular, Julia also has built in parallel capabilities which can be taken advantage of.
For documentation of the Julia language, see this link: Julia documentation