Proposal to add `spconform` to the Spatial Task View
Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 55/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- r
- Domain
- documentation
Research direction
Review the Spatial CRAN Task View and the linked spconform CRAN package, GitHub repository, and vignette first. Confirm the package's fit and placement with the maintainers, then add the proposed spconform entry if accepted; done means the task view includes accurate package wording and links.
Written by the indexing model from the issue text.
Description
Hi @rsbivand, @Nowosad, and @kadyb,
I recently published a package called spconform on CRAN and was hoping you might consider it for the Spatial Task View.
In short, the package provides distribution-free, model-agnostic prediction intervals for spatial data using localized conformal calibration (based on Mao, Martin and Reich, 2024). It handles both point-referenced and areal data, and it relaxes the standard exchangeability assumption by using spatial proximity.
I designed it to be as lightweight as possible. It has zero external dependencies (imports only base stats), so it’s very stable and passed all CRAN checks without issues. I also included a diagnose() function to evaluate things like empirical coverage, boundary effects, and spatial residual autocorrelation.
Links:
- CRAN: https://CRAN.R-project.org/package=spconform
- GitHub: https://github.com/amjed-droid/spconform
- Vignette: https://cran.r-project.org/web/packages/spconform/vignettes/spconform-intro.html
I think it could naturally fit into either the "Machine learning of spatial data" section (since it wraps ML predictors) or "Geostatistics". If you think it's a good fit, here is a potential line to include:
- The
r pkg("spconform")package provides distribution-free, model-agnostic prediction intervals for spatial and spatio-temporal data using localized conformal calibration, along with spatial diagnostic tools.
Thanks for your time and for maintaining this view.
Best,
Ahmed
- Dominant language
- No language data
- Stars
- 57
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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