spmodel: Spatial Statistical Modeling and Prediction (original) (raw)
Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
| Version: | 0.11.1 |
|---|---|
| Depends: | R (≥ 3.5.0) |
| Imports: | graphics, generics, Matrix, sf, stats, tibble, parallel |
| Suggests: | rmarkdown, knitr, testthat (≥ 3.0.0), ggplot2, ranger, statmod, pROC, emmeans (≥ 1.4), estimability |
| Published: | 2025-11-14 |
| DOI: | 10.32614/CRAN.package.spmodel |
| Author: | Michael Dumelle  [aut, cre], Matt Higham [aut], Ryan A. Hill [ctb], Michael Mahon [ctb], Jay M. Ver Hoef [aut] |
| Maintainer: | Michael Dumelle <Dumelle.Michael at epa.gov> |
| BugReports: | https://github.com/USEPA/spmodel/issues |
| License: | GPL-3 |
| URL: | https://usepa.github.io/spmodel/ |
| NeedsCompilation: | no |
| Citation: | spmodel citation info |
| Materials: | README, NEWS |
| In views: | MixedModels, Spatial |
| CRAN checks: | spmodel results |
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