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spmodel: Spatial statistical modeling and prediction in .

Michael Dumelle1, Matt Higham2, Jay M Ver Hoef3

  • 1United States Environmental Protection Agency, Corvallis, Oregon, United States of America.

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Summary
This summary is machine-generated.

The spmodel package offers tools for spatial statistical modeling of point-referenced and areal data. It facilitates model fitting, summarization, and prediction using various estimation methods and advanced features.

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Area of Science:

  • Spatial statistics
  • Geostatistics
  • Statistical modeling

Background:

  • Spatial data analysis is crucial in many scientific fields.
  • Existing methods may lack comprehensive features for complex spatial models.
  • Efficient tools are needed for fitting, summarizing, and predicting spatial data.

Purpose of the Study:

  • Introduce the spmodel package for spatial statistical analysis.
  • Provide a versatile tool for various spatial modeling tasks.
  • Enable accurate predictions for unobserved locations.

Main Methods:

  • Parameter estimation via likelihood-based optimization and weighted least squares using variograms.
  • Incorporation of anisotropy, non-spatial random effects, and partition factors.
  • Application of big data approaches for large datasets.

Main Results:

  • spmodel facilitates fitting, summarizing, and predicting spatial models.
  • Model-fit statistics aid in model comparison and visualization.
  • The package supports advanced features for complex spatial data.

Conclusions:

  • spmodel is a comprehensive R package for spatial statistical modeling.
  • It offers robust methods for parameter estimation and prediction.
  • The package enhances the analysis of point-referenced and areal data.