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spmodel: Spatial statistical modeling and prediction in [Image: see text]

spmodel is an [Image: see text] package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variog...

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Detalles Bibliográficos
Autores principales: Dumelle, Michael, Higham, Matt, Ver Hoef, Jay M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997982/
https://www.ncbi.nlm.nih.gov/pubmed/36893090
http://dx.doi.org/10.1371/journal.pone.0282524
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author Dumelle, Michael
Higham, Matt
Ver Hoef, Jay M.
author_facet Dumelle, Michael
Higham, Matt
Ver Hoef, Jay M.
author_sort Dumelle, Michael
collection PubMed
description spmodel is an [Image: see text] package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms. 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.
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spelling pubmed-99979822023-03-10 spmodel: Spatial statistical modeling and prediction in [Image: see text] Dumelle, Michael Higham, Matt Ver Hoef, Jay M. PLoS One Research Article spmodel is an [Image: see text] package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms. 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. Public Library of Science 2023-03-09 /pmc/articles/PMC9997982/ /pubmed/36893090 http://dx.doi.org/10.1371/journal.pone.0282524 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Dumelle, Michael
Higham, Matt
Ver Hoef, Jay M.
spmodel: Spatial statistical modeling and prediction in [Image: see text]
title spmodel: Spatial statistical modeling and prediction in [Image: see text]
title_full spmodel: Spatial statistical modeling and prediction in [Image: see text]
title_fullStr spmodel: Spatial statistical modeling and prediction in [Image: see text]
title_full_unstemmed spmodel: Spatial statistical modeling and prediction in [Image: see text]
title_short spmodel: Spatial statistical modeling and prediction in [Image: see text]
title_sort spmodel: spatial statistical modeling and prediction in [image: see text]
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997982/
https://www.ncbi.nlm.nih.gov/pubmed/36893090
http://dx.doi.org/10.1371/journal.pone.0282524
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