spmodel: Spatial statistical modeling and prediction in R

Michael Dumelle, Matt Higham, Jay M. Ver Hoef

PLoS ONE · 2023 · 27 citations · 23 references

DOIFull text

Open access

Concepts

Abstract

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.

References

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