Add icdf functions for Moyal, Gumbel, Triangular and Weibull distributions

[amyoshino]

What is this PR about?
Adds ICDF functions to Moyal, Gumbel, Triangular and Weibull distributions

Issue #6612

References:
Moyal: https://www.wolframalpha.com/input?i=InverseCDF%5BMoyal+Distribution%5D
Gumbel: https://en.wikipedia.org/wiki/Gumbel_distribution ["Random variate generation" section]
Triangular: http://www.math.wm.edu/~leemis/chart/UDR/PDFs/TriangularV.pdf
Weibull: https://www.mathworks.com/help/stats/wblinv.html

Checklist

• Explain important implementation details 👆
• Make sure that the pre-commit linting/style checks pass.
• Link relevant issues (preferably in nice commit messages)
• Are the changes covered by tests and docstrings?
• Fill out the short summary sections 👇

Major / Breaking Changes

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New features

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Bugfixes

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Documentation

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Maintenance

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📚 Documentation preview 📚: https://pymc--6802.org.readthedocs.build/en/6802/

[codecov]

Codecov Report

Merging #6802 (928c747) into main (7b08fc1) will decrease coverage by 2.16%.
The diff coverage is 100.00%.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #6802      +/-   ##
==========================================
- Coverage   91.92%   89.77%   -2.16%     
==========================================
  Files          95       95              
  Lines       16197    16213      +16     
==========================================
- Hits        14889    14555     -334     
- Misses       1308     1658     +350     

Impacted Files	Coverage Δ
pymc/distributions/continuous.py	97.81% <100.00%> (+0.03%)	⬆️

... and 6 files with indirect coverage changes

[ricardoV94]

Looks great!