spmodel

spmodel-package

<img src="figures/logo.png?package=spmodel&version=0.10.0" style="float: right" alt="logo" width="120">
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) tools:::Rd_expr_doi("10.1371/journal.pone.0282524").

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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>.

Michael Dumelle

Spatial Statistical Modeling and Prediction

Matt Higham

Ryan A. Hill

Michael Mahon

Jay M. Ver Hoef

spmodel-package function

Maintainer: Michael Dumelle <a href="/link/Dumelle.Michael%40epa.gov?package=spmodel&version=0.10.0" data-mini-rdoc="spmodel::Dumelle.Michael@epa.gov">Dumelle.Michael@epa.gov</a> (<a href="https://orcid.org/0000-0002-3393-5529">ORCID</a>)
Authors:<ul>
<li>Matt Higham <a href="/link/mhigham%40stlawu.edu?package=spmodel&version=0.10.0" data-mini-rdoc="spmodel::mhigham@stlawu.edu">mhigham@stlawu.edu</a> (<a href="https://orcid.org/0009-0006-4217-625X">ORCID</a>)</li>
<li>Jay M. Ver Hoef <a href="/link/jay.verhoef%40noaa.gov?package=spmodel&version=0.10.0" data-mini-rdoc="spmodel::jay.verhoef@noaa.gov">jay.verhoef@noaa.gov</a> (<a href="https://orcid.org/0000-0003-4302-6895">ORCID</a>)</li>
</ul>Other contributors:<ul>
<li>Ryan A. Hill <a href="/link/hill.ryan%40epa.gov?package=spmodel&version=0.10.0" data-mini-rdoc="spmodel::hill.ryan@epa.gov">hill.ryan@epa.gov</a> (<a href="https://orcid.org/0000-0001-9583-0426">ORCID</a>) [contributor]</li>
<li>Michael Mahon <a href="/link/Mahon.Michael%40epa.gov?package=spmodel&version=0.10.0" data-mini-rdoc="spmodel::Mahon.Michael@epa.gov">Mahon.Michael@epa.gov</a> (<a href="https://orcid.org/0000-0002-9436-2998">ORCID</a>) [contributor]</li>
</ul>

Author

<img src='figures/logo.png' style='float: right' alt='logo' width='120' />
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) tools:::Rd_expr_doi("10.1371/journal.pone.0282524").

spmodel: Spatial Statistical Modeling and Prediction — spmodel-package

Maintainer: Michael Dumelle <a href='mailto:Dumelle.Michael@epa.gov'>Dumelle.Michael@epa.gov</a> (<a href='https://orcid.org/0000-0002-3393-5529'>ORCID</a>)
Authors:<ul>
<li>Matt Higham <a href='mailto:mhigham@stlawu.edu'>mhigham@stlawu.edu</a> (<a href='https://orcid.org/0009-0006-4217-625X'>ORCID</a>)</li>
<li>Jay M. Ver Hoef <a href='mailto:jay.verhoef@noaa.gov'>jay.verhoef@noaa.gov</a> (<a href='https://orcid.org/0000-0003-4302-6895'>ORCID</a>)</li>
</ul>

Other contributors:<ul>
<li>Ryan A. Hill <a href='mailto:hill.ryan@epa.gov'>hill.ryan@epa.gov</a> (<a href='https://orcid.org/0000-0001-9583-0426'>ORCID</a>) [contributor]</li>
<li>Michael Mahon <a href='mailto:Mahon.Michael@epa.gov'>Mahon.Michael@epa.gov</a> (<a href='https://orcid.org/0000-0002-9436-2998'>ORCID</a>) [contributor]</li>
</ul>

spmodel-package: spmodel: Spatial Statistical Modeling and Prediction

Description

Arguments

Author

See Also