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: 0.11.0 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: 2025-07-03 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 Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=spmodel to link to this page.
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