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spmodel: Spatial Statistical Modeling and Prediction in R

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  • Overview
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.

Impact/Purpose

This presentation will provide guidance for using spatial statistical models. These models provide ecological insights while explicitly incorporating spatial dependence. Often they notably improve model fitting and prediction compared to nonspatial models.

Citation

Dumelle, M., M. Higham, AND J. Ver Hoef. spmodel: Spatial Statistical Modeling and Prediction in R. 2024 ASA Statistics and the Environment Workshop, Boulder, CO, October 03 - 05, 2024.
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Last updated on March 06, 2025
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