Gstat is an open source (GPL) computer code for multivariable geostatistical modelling, prediction and simulation. It can calculate sample variograms, fit valid models, show variograms, calculate (pseudo) cross variograms, fit valid linear models of coregionalization (S extension only), and calculate and fit directional variograms and variogram models (anisotropy coefficients are not fitted automatically). Kriging and (sequential) conditional simulation are done under (simplifications of) the universal cokriging model. Any number of variables may be spatially cross-correlated. Each variable may have its own number of trend functions specified (being coordinates, or so-called external drift variables). Simplifications of this model include ordinary and simple kriging, ordinary or simple cokriging, universal kriging, external drift kriging, Gaussian conditional or unconditional simulation or cosimulation. In addition, variables may share trend coefficients (e.g. for collocated cokriging).

References in zbMATH (referenced in 21 articles )

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  1. Allard, Denis; Hristopulos, Dionisios T.; Opitz, Thomas: Linking physics and spatial statistics: a new family of Boltzmann-Gibbs random fields (2021)
  2. Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
  3. Arnone, Eleonora; Azzimonti, Laura; Nobile, Fabio; Sangalli, Laura M.: Modeling spatially dependent functional data via regression with differential regularization (2019)
  4. Liu, Yang; Li, Jingfa; Sun, Shuyu; Yu, Bo: Advances in Gaussian random field generation: a review (2019)
  5. Mastrantonio, Gianluca; Lasinio, Giovanna Jona; Pollice, Alessio; Capotorti, Giulia; Teodonio, Lorenzo; Genova, Giulio; Blasi, Carlo: A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles (2019)
  6. Bernardi, Mara S.; Carey, Michelle; Ramsay, James O.; Sangalli, Laura M.: Modeling spatial anisotropy via regression with partial differential regularization (2018)
  7. Philipp Otto: spGARCH: An R-Package for Spatial and Spatiotemporal ARCH models (2018) arXiv
  8. McClintock, Brett T.: Incorporating telemetry error into hidden Markov models of animal movement using multiple imputation (2017)
  9. Blangiardo, Marta; Cameletti, Michela: Spatial and spatio-temporal Bayesian models with R-INLA (2015)
  10. Erhardt, Tobias Michael; Czado, Claudia; Schepsmeier, Ulf: Spatial composite likelihood inference using local C-vines (2015)
  11. Genton, Marc G.; Kleiber, William: Rejoinder (2015)
  12. Menictas, Marianne; Wand, Matt P.: Variational inference for heteroscedastic semiparametric regression (2015)
  13. Harris, Paul; Brunsdon, Chris; Charlton, Martin; Juggins, Steve; Clarke, Annemarie: Multivariate spatial outlier detection using robust geographically weighted methods (2014)
  14. Kang, Su Yun; Mcgree, James; Baade, Peter; Mengersen, Kerrie: An investigation of the impact of various geographical scales for the specification of spatial dependence (2014)
  15. van den Boogaart, K. Gerald; Tolosana-Delgado, Raimon: Analyzing compositional data with R (2013)
  16. Giraldo, R.; Delicado, P.; Mateu, J.: Continuous time-varying Kriging for spatial prediction of functional data: an environmental application (2010)
  17. 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)
  18. Garška, R.; Krūminienė, I.: Spatial analysis and prediction of Curonian lagoon data with Gstat (2004)
  19. Garška, R.; Krūminienė, I.: Spatial analysis and prediction of Curonian lagoon data with gstat (2004)
  20. Lesauskienė, E.; Dučinskas, K.: Universal kriging for spatio-temporal data (2003)

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