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spmodel: Spatial Statistical Modeling and Prediction in R
Citation:
Dumelle, M., M. Higham, AND J. Ver Hoef. spmodel: Spatial Statistical Modeling and Prediction in R. PLOS ONE . Public Library of Science, San Francisco, CA, 18(3):e0282524, (2023). https://doi.org/10.1371/journal.pone.0282524
Impact/Purpose:
Spatial statistical models are an incredibly useful tool for assessing the importance of environmental drivers and to make predictions for spatial data. Unfortunately, these models are notoriously challenging, both theoretically and computationally. spmodel will provide a convenient, easy-to-use front-end for spatial statistical models. A variety of covariance functions, estimation methods, and model specifications are available. spmodel can apply these models to data having millions of observations. spmodel focuses on making the user experience easy and efficient – accessible to a wide audience.
Description:
spmodel is an R package used to fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced or 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.
