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 24 articles )

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  1. Emanuele Giorgi and Peter Diggle: PrevMap: An R Package for Prevalence Mapping (2017)
  2. Guido Masarotto and Cristiano Varin: Gaussian Copula Regression in R (2017)
  3. Sandra de Iaco: The cgeostat Software for Analyzing Complex-Valued Random Fields (2017)
  4. Acosta, Jonathan; Osorio, Felipe; Vallejos, Ronny: Effective sample size for line transect sampling models with an application to marine macroalgae (2016)
  5. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: A comparison of spatial predictors when datasets could be very large (2016)
  6. Blangiardo, Marta; Cameletti, Michela: Spatial and spatio-temporal Bayesian models with R-INLA (2015)
  7. Bradley, Jonathan R.; Cressie, Noel; Shi, Tao: Comparing and selecting spatial predictors using local criteria (2015)
  8. Mark D. Risser, Catherine A. Calder: Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R (2015) arXiv
  9. Zachary D. Weller: spTest: An R Package Implementing Nonparametric Tests of Isotropy (2015) arXiv
  10. Giraldo, Ramón; Mateu, Jorge; Delicado, Pedro: geofd: an R package for function-valued geostatistical prediction (2012)
  11. Calder, Catherine A.; Berrett, Candace; Shi, Tao; Xiao, Ningchuan; Munroe, Darla K.: Modeling space-time dynamics of aerosols using satellite data and atmospheric transport model output (2011)
  12. Miranda, Hilário; Souto de Miranda, Manuela: Combining robustness with efficiency in the estimation of the variogram (2011)
  13. Verzelen, Nicolas: Data-driven neighborhood selection of a Gaussian field (2010)
  14. Ginsbourger, David; Dupuy, Delphine; Badea, Anca; Carraro, Laurent; Roustant, Olivier: A note on the choice and the estimation of kriging models for the analysis of deterministic computer experiments (2009)
  15. López-Quílez, Antonio; Muñoz, Facundo: Geostatistical computing of acoustic maps in the presence of barriers (2009)
  16. Ruppert, David; Wand, M.P.; Carroll, Raymond J.: Semiparametric regression during 2003--2007 (2009)
  17. White, Gentry; Ghosh, Sujit K.: A stochastic neighborhood conditional autoregressive model for spatial data (2009)
  18. Zuur, Alain F.; Ieno, Elena N.; Walker, Neil J.; Saveliev, Anatoly A.; Smith, Graham M.: Mixed effects models and extensions in ecology with R (2009)
  19. Hartman, Linda; Hössjer, Ola: Fast Kriging of large data sets with Gaussian Markov random fields (2008)
  20. Krause, Andreas; Singh, Ajit; Guestrin, Carlos: Near-optimal sensor placements in Gaussian processes: theory, efficient algorithms and empirical studies (2008)

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