geoR

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: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 41 articles )

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  1. Diggle, Peter J.; Giorgi, Emanuele: Model-based geostatistics for global public health. Methods and applications (2019)
  2. Bernardi, Mara S.; Carey, Michelle; Ramsay, James O.; Sangalli, Laura M.: Modeling spatial anisotropy via regression with partial differential regularization (2018)
  3. Fagundes, R. S.; Uribe-Opazo, M. A.; Galea, M.; Guedes, L. P. C.: Spatial variability in slash linear modeling with finite second moment (2018)
  4. Vicario, Grazia; Pistone, Giovanni: Simulated variogram-based error inspection of manufactured parts (2018)
  5. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  6. Emanuele Giorgi and Peter Diggle: PrevMap: An R Package for Prevalence Mapping (2017) not zbMATH
  7. Guido Masarotto and Cristiano Varin: Gaussian Copula Regression in R (2017) not zbMATH
  8. Sandra de Iaco: The cgeostat Software for Analyzing Complex-Valued Random Fields (2017) not zbMATH
  9. Acosta, Jonathan; Osorio, Felipe; Vallejos, Ronny: Effective sample size for line transect sampling models with an application to marine macroalgae (2016)
  10. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: A comparison of spatial predictors when datasets could be very large (2016)
  11. Andrew Finley; Sudipto Banerjee; Alan Gelfand: spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models (2015) not zbMATH
  12. Blangiardo, Marta; Cameletti, Michela: Spatial and spatio-temporal Bayesian models with R-INLA (2015)
  13. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: Comparing and selecting spatial predictors using local criteria (2015)
  14. De Oliveira, Victor; Kone, Bazoumana: Prediction intervals for integrals of Gaussian random fields (2015)
  15. Khandoker Bakar; Sujit Sahu: spTimer: Spatio-Temporal Bayesian Modeling Using R (2015) not zbMATH
  16. Liang Jing; Victor De Oliveira: geoCount: An R Package for the Analysis of Geostatistical Count Data (2015) not zbMATH
  17. Mark D. Risser, Catherine A. Calder: Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R (2015) arXiv
  18. Martin Schlather; Alexander Malinowski; Peter Menck; Marco Oesting; Kirstin Strokorb: Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields (2015) not zbMATH
  19. Patrick Brown: Model-Based Geostatistics the Easy Way (2015) not zbMATH
  20. Zachary D. Weller: spTest: An R Package Implementing Nonparametric Tests of Isotropy (2015) arXiv

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