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: 0.5.1
Depends: R (≥ 3.5.0)
Imports: graphics, generics, Matrix, sf, stats, tibble, parallel
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), ggplot2, ranger, statmod
Published: 2024-01-09
Author: Michael Dumelle ORCID iD [aut, cre], Matt Higham [aut], 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


Reference manual: spmodel.pdf
Vignettes: An Introduction to spmodel


Package source: spmodel_0.5.1.tar.gz
Windows binaries: r-prerel: spmodel_0.5.1.zip, r-release: spmodel_0.5.1.zip, r-oldrel: spmodel_0.5.1.zip
macOS binaries: r-prerel (arm64): spmodel_0.5.1.tgz, r-release (arm64): spmodel_0.5.1.tgz, r-oldrel (arm64): spmodel_0.5.1.tgz, r-prerel (x86_64): spmodel_0.5.1.tgz, r-release (x86_64): spmodel_0.5.1.tgz
Old sources: spmodel archive

Reverse dependencies:

Reverse imports: SSN2


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