Proposal to add `spconform` to the Spatial Task View

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@amjed-droid 已经在做这个了。

开始于 2026年10月9日。

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文档
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r
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调研方向

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.

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描述

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:

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

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这个项目没有提供开发容器、Dockerfile 或贡献指南，环境需要你自己搭建：先看它的 README，通用步骤见我们的新手贡献指南。

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  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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