Add a Gibbs sampler for the regularized logistic regression

[rlouf]

This PR adds a Gibbs sampler for the regularized binary logistic regression, as presented in Makalik & Schmidt (2016). The change from the regularized negative binomial regression is trivial, and I opened this PR to start thinking about how modular the implementation needs to be.

Closes #12, and we should aim to close #11 as well.

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[codecov]

Codecov Report

Merging #13 (5e8171d) into main (f42e0e8) will not change coverage.
The diff coverage is 100.00%.

@@            Coverage Diff            @@
##              main       #13   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files            2         2           
  Lines           99       128   +29     
  Branches         6        10    +4     
=========================================
+ Hits            99       128   +29     

Impacted Files	Coverage Δ
aemcmc/gibbs.py	100.00% <100.00%> (ø)

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[brandonwillard]

I'm fine with this refactoring. Just don't be surprised if it's refactored again soon, because this project is likely to undergo large changes quickly.

[zoj613]

Is this ready for review or should I wait till its no longer in draft state? @rlouf

[rlouf]

@zoj613 Feel free to review while I pull the changes from main. The style checks are failing because of a problem with the version of black used in pre-commit. I bumped the version in #18 .

[rlouf]

@zoj613 All checks pass now. Waiting for your review to merge.