spmodel: Spatial Statistical Modeling and Prediction
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: |
0.9.0 |
| 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: |
2024-11-06 |
| 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: |
Spatial |
| CRAN checks: |
spmodel results |
Documentation:
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