statsmodels / statsmodels/statsmodels
distance covariance and correlation
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- Dominant language
- Python
- Stars
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- Forks
- 3.6k
- Avg merge
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- Merged PRs (30d)
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Description
Just wanted to park your gist here since I came across it this week, looking for similar. I don't know if it's something we want to include or if it belongs in scipy or scikit-learn (I don't think it's already there?).
https://gist.github.com/josef-pkt/2938402
There's a O(n) memory and O(n*log(n)) algorithm here that wouldn't be too hard to implement (and put into Cython)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked gist and the arXiv paper to understand the proposed distance covariance and correlation algorithms. Determine whether the work belongs in statsmodels, scipy, or scikit-learn; done would require a settled project destination and an agreed implementation scope.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- analytics, data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100