The package geoR provides functions for geostatistical data analysis using the software R. This document illustrates some (but not all!) of the capabilities of the package. The objective is to familiarise the reader with the geoR’s commands for data analysis and show some of the graphical outputs which can be produced. The commands used here are just illustrative, providing basic examples of the package handling. We did not attempt to perform a definitive analysis of the data-set used throughout the exemples neither to cover all the details of the package capability. In what follows: the R commands are shown in slanted typewriter fonts like this, the corresponding output, if any, is shown in typewriter fonts like this. Typically, default arguments are used for the function calls and the user is encouraged to inspect other arguments of the functions using the args and help functions. For instance, to see all the arguments for the function variog type args(variog) and/or help(variog). (Source:

References in zbMATH (referenced in 50 articles )

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  1. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  2. Claudia Cappello, Sandra De Iaco, Donato Posa: covatest: An R Package for Selecting a Class of Space-Time Covariance Functions (2020) not zbMATH
  3. Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
  4. Atkinson, Peter M.; Mateu, Jorge: A conversation with Peter Diggle (2019)
  5. Davis, Benjamin J. K.; Curriero, Frank C.: Development and evaluation of geostatistical methods for non-Euclidean-based spatial covariance matrices (2019)
  6. Diggle, Peter J.; Giorgi, Emanuele: Model-based geostatistics for global public health. Methods and applications (2019)
  7. Rasch, Dieter; Verdooren, Rob; Pilz, Jürgen: Applied statistics. Theory and problem solutions with R (2019)
  8. Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun: ExaGeoStatR: A Package for Large-Scale Geostatistics in R (2019) arXiv
  9. Bernardi, Mara S.; Carey, Michelle; Ramsay, James O.; Sangalli, Laura M.: Modeling spatial anisotropy via regression with partial differential regularization (2018)
  10. Fagundes, R. S.; Uribe-Opazo, M. A.; Galea, M.; Guedes, L. P. C.: Spatial variability in slash linear modeling with finite second moment (2018)
  11. Giraldo, Ramón; Caballero, William; Camacho-Tamayo, Jesús: Mantel test for spatial functional data. An application to infiltration curves (2018)
  12. Vicario, Grazia; Pistone, Giovanni: Simulated variogram-based error inspection of manufactured parts (2018)
  13. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  14. Emanuele Giorgi and Peter Diggle: PrevMap: An R Package for Prevalence Mapping (2017) not zbMATH
  15. Guido Masarotto and Cristiano Varin: Gaussian Copula Regression in R (2017) not zbMATH
  16. Sandra de Iaco: The cgeostat Software for Analyzing Complex-Valued Random Fields (2017) not zbMATH
  17. Acosta, Jonathan; Osorio, Felipe; Vallejos, Ronny: Effective sample size for line transect sampling models with an application to marine macroalgae (2016)
  18. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: A comparison of spatial predictors when datasets could be very large (2016)
  19. Andrew Finley; Sudipto Banerjee; Alan Gelfand: spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models (2015) not zbMATH
  20. Blangiardo, Marta; Cameletti, Michela: Spatial and spatio-temporal Bayesian models with R-INLA (2015)

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