R package fields: Tools for spatial data. Fields is for curve, surface and function fitting with an emphasis on splines, spatial data and spatial statistics. The major methods include cubic, and thin plate splines, Kriging and compact covariances for large data sets. The splines and Kriging methods are supporting by functions that can determine the smoothing parameter (nugget and sill variance) by cross validation and also by restricted maximum likelihood. A major feature is that any covariance function implemented in R and following a simple fields format can be used for spatial prediction. Some tailored optimization functions are supplied for finding the MLEs for the Matern family of covariances. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. But spam is not required for the standard spatial functions. Use help(fields) to get started and for an overview. The fields source code is heavily commented and provides useful explanations of numerical details in addition to the manual pages.

References in zbMATH (referenced in 45 articles )

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  1. Kirsner, Daniel; Sansó, Bruno: Multi-scale shotgun stochastic search for large spatial datasets (2020)
  2. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  3. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  4. Huang, Whitney K.; Cooley, Daniel S.; Ebert-Uphoff, Imme; Chen, Chen; Chatterjee, Snigdhansu: New exploratory tools for extremal dependence: (\chi) networks and annual extremal networks (2019)
  5. Morris, Samuel A.; Reich, Brian J.; Thibaud, Emeric: Exploration and inference in spatial extremes using empirical basis functions (2019)
  6. Mukhopadhyay, Sabyasachi; Ogutu, Joseph O.; Bartzke, Gundula; Dublin, Holly T.; Piepho, Hans-Peter: Modelling spatio-temporal variation in sparse rainfall data using a hierarchical Bayesian regression model (2019)
  7. Overstall, Antony M.; Woods, David C.; Martin, Kieran J.: Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics (2019)
  8. Robert Geitner and Robby Fritzsch and Jürgen Popp and Thomas Bocklitz: corr2D: Implementation of Two-Dimensional Correlation Analysis in R (2019) not zbMATH
  9. 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
  10. Bernardi, Mara S.; Carey, Michelle; Ramsay, James O.; Sangalli, Laura M.: Modeling spatial anisotropy via regression with partial differential regularization (2018)
  11. David Bolin; Finn Lindgren: Calculating Probabilistic Excursion Sets and Related Quantities Using excursions (2018) not zbMATH
  12. Dombry, Clément; Ribatet, Mathieu; Stoev, Stilian: Probabilities of concurrent extremes (2018)
  13. Micheas, Athanasios C.; Chen, Jiaxun: sppmix: Poisson point process modeling using normal mixture models (2018)
  14. Benjamin Taylor and Barry Rowlingson: spatsurv: An R Package for Bayesian Inference with Spatial Survival Models (2017) not zbMATH
  15. Camilo Jose Torres-Jimenez, Alvaro Mauricio Montenegro Diaz: An alternative to continuous univariate distributions supported on a bounded interval: The BMT distribution (2017) arXiv
  16. Falke, Andreas; Hruschka, Harald: A Monte Carlo study of design-generating algorithms for the latent class mixed logit model (2017)
  17. Marcello Chiodi and Giada Adelfio: Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes’ Description (2017) not zbMATH
  18. Matthew Dixon, Diego Klabjan, Lan Wei: OSTSC: Over Sampling for Time Series Classification in R (2017) arXiv
  19. Risk, J.; Ludkovski, M.: Statistical emulators for pricing and hedging longevity risk products (2016)
  20. Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016) not zbMATH

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