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, 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.

Version: 0.2.0
Depends: R (≥ 3.5.0)
Imports: graphics, generics, Matrix, sf, stats, tibble, parallel
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), ggplot2, ranger
Published: 2022-11-11
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
CRAN checks: spmodel results


Reference manual: spmodel.pdf
Vignettes: An Overview of Basic Features in spmodel
A Detailed Guide to spmodel
Technical Details


Package source: spmodel_0.2.0.tar.gz
Windows binaries: r-devel: spmodel_0.2.0.zip, r-release: spmodel_0.2.0.zip, r-oldrel: spmodel_0.2.0.zip
macOS binaries: r-release (arm64): spmodel_0.2.0.tgz, r-oldrel (arm64): spmodel_0.2.0.tgz, r-release (x86_64): spmodel_0.2.0.tgz, r-oldrel (x86_64): spmodel_0.2.0.tgz
Old sources: spmodel archive


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