parallelization simulation based indexes and inference wrappers

[renanxcortes]

• Some indexes such as Modified Dissimilarity (Dct), Modified Gini (Gct) and Bias-Corrected Dissimilarity (Dbc) could be leveraged to work in parallel since they rely on independent draws of probability distributions and recalculating the index.

• Also, the inference wrappers (Infer_Segregation and Compare_Segregation) could be leveraged to work in parallel since they rely on independent simulations framework.

A possibility to implement is to use Dask (https://github.com/dask/dask), concurrent.futures (https://docs.python.org/3/library/concurrent.futures.html), etc.

[knaaptime]

this might be relevant

which also raises the issue that we might want to allow a seed argument in the current implementation

[renanxcortes]

I was just surfing on the web reading some posts and read something that let me wondering if we could use some kind of just-in-time for these functions: http://numba.pydata.org/numba-doc/0.17.0/user/jit.html or something similar to what it is possible to do in R with for loops with the compiler package: https://www.r-statistics.com/2012/04/speed-up-your-r-code-using-a-just-in-time-jit-compiler/

[knaaptime]

i'm familiar with numba in concept but never been sure what kind of code its able to speed up. maybe we could try some speed tests?

[knaaptime]

solved for inference classes by #174

[knaaptime]

resolved by #183