Interesting package: anticlust
Nobody has claimed this yet.
- Dominant language
- R
- Stars
- 10
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Description
[anticlust](https://github.com/m-Py/anticlust) aims to something similar to what I do here. It is also on CRAN (3 months earlier than experDesign).
It has a strong(er) mathematical foundation: instead of randomly testing it uses some (smart?) algorithms.
It handles a bit worse the categorical variables but the authors have also worked on that.
In summary, it is a good package worth mentioning on the README (and perhaps to depend on it to extend it for spatial/plate distribution).
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 with the repository README and review the linked anticlust package to determine what comparison is useful to readers. Update the README to mention anticlust and clarify whether the optional dependency idea is in scope; done means the package is accurately described without extending project behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- r
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100