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Spatial Statistical Modeling and Prediction in R
Citation:
Dumelle, M., M. Higham, AND J. Ver Hoef. Spatial Statistical Modeling and Prediction in R. U.S. Environmental Protection Agency, Washington, DC, 2022.
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. Parameters of spatial linear models and spatial autoregressive models are estimated using a variety of 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 easily obtainable.
