[ENH] Logistic distribution

[malikrafsan]

Reference Issues/PRs

#22

What does this implement/fix? Explain your changes.

Logistic probability distribution

Does your contribution introduce a new dependency? If yes, which one?

no

What should a reviewer concentrate their feedback on?

• Energy formula for logistic distribution

Did you add any tests for the change?

• Yes, I try to install this library locally using pip install --editable .[dev,test] and create simple driver program to use this new distribution

Any other comments?

• I use the formula from Wikipedia, which can be accessed here
• For log_pdf, I use wolfram alpha to derive the formula, here is the screenshot
  

PR checklist

For all contributions

• I've added myself to the list of contributors with any new badges I've earned :-)
  How to: add yourself to the all-contributors file in the skpro root directory (not the CONTRIBUTORS.md). Common badges: code - fixing a bug, or adding code logic. doc - writing or improving documentation or docstrings. bug - reporting or diagnosing a bug (get this plus code if you also fixed the bug in the PR).maintenance - CI, test framework, release.
  See here for full badge reference
• The PR title starts with either [ENH], [MNT], [DOC], or [BUG]. [BUG] - bugfix, [MNT] - CI, test framework, [ENH] - adding or improving code, [DOC] - writing or improving documentation or docstrings.

For new estimators

• I've added the estimator to the API reference - in docs/source/api_reference/taskname.rst, follow the pattern.
• I've added one or more illustrative usage examples to the docstring, in a pydocstyle compliant Examples section.
• If the estimator relies on a soft dependency, I've set the python_dependencies tag and ensured
  dependency isolation, see the estimator dependencies guide.

[fkiraly]

For log_pdf, I use wolfram alpha to derive the formula

Interesting - I would just have used the standard logarithm rules to cancel exps etc.

Do you have an opinion on which representation is more stable, numerically?

Intuitively, at least, I would think cancelling as many exps as possible is better.