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Home Page: https://www.pywhy.org/pywhy-stats/
License: MIT License
Python package for (conditional) independence testing and statistical functions related to causality.
Home Page: https://www.pywhy.org/pywhy-stats/
License: MIT License
We should have an example demonstrating how to leverage different kernels. We can ideally show how different kernels are useful too on a small simulated data.
Originally posted by @adam2392 in #15 (comment)
Is your feature request related to a problem? Please describe.
Similar to numpy, scipy and scikit-learn and many other performant libraries, we should implement an ASV benchmarking suite that will then allow us to gauge how to improve the runtime and memory performance of the CI test suite. This will then map to significant improvements downstream in dowhy, dodiscover, etc.
Describe the solution you'd like
Add asv benchmarks. See scikit-learn repo for some boilerplate.
Additional context
We need to figure out where to host the resulting plots and add a CI pipeline to handle this. Not sure exactly on details related to this yet tho...
Migrate over basic documentation and poetry setup from dodiscover
Systematically migrate independence test, documentation and unit tests one at a time.
Is your feature request related to a problem? Please describe.
Hey I just wanted to let you know the links for the documentation are broken in the readme. Also the "installation page" link
Describe the solution you'd like
The documentation if it is done, or some kind of flag like WIP if it is undone or it doesn't exist
As brought up in #19, the power_divergence
should just take the stance that categorical features can be str
main
branch.pip freeze
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Python version:
pip freeze
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See: larsoner/circleci-artifacts-redirector-action#40
TODO:
main
branch.pip freeze
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OS:
Python version:
pip freeze
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