[WIP] distribution classes

[pavanramkumar]

closes #350

TODOs

• [x] make it work for all distribution classes
• [x] add tests for BaseDistribution class
• [x] make tests and examples pass locally
• [x] remove unused hidden methods from pyglmnet.py (currently commented out)
• [x] migrate simulate_glm() functionality into simulate() methods of respective classes
• [ ] migrate metrics definitions that depend on log_likelihood into distribution classes (pseudo_R2, deviance)
• [ ] add an example to show how users should roll out their own distribution class (e.g., using jax numpy)
• [ ] ensure Probit and Binomial work correctly
• [ ] check if all docstrings still make sense
• [x] update API reference
• [ ] update what's new

[geektoni]

:woman_dancing: :woman_dancing:

[jasmainak]

exciting stuff! I think it significantly improves our repository :-D

I would love to see an example with autograd or something like that

[geektoni]

Quick question. If I wanted to contribute on this one here, should I fork your repo @pavanramkumar or do you know if there is any way to push directly to this PR?

[jasmainak]

I think forking might be the easiest. But maybe @pavanramkumar should confirm first if he has any local changes that have not been pushed?

[pavanramkumar]

Thanks for picking this up @geektoni ! Please go ahead and fork.