fields

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 52 articles )

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  1. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  2. Huang, Hengzhen; Chen, Xueping: Compromise design for combination experiment of two drugs (2021)
  3. Raim, Andrew M.; Holan, Scott H.; Bradley, Jonathan R.; Wikle, Christopher K.: Spatio-temporal change of support modeling with \textttR (2021)
  4. Brantley, Halley L.; Guinness, Joseph; Chi, Eric C.: Baseline drift estimation for air quality data using quantile trend filtering (2020)
  5. Kirsner, Daniel; Sansó, Bruno: Multi-scale shotgun stochastic search for large spatial datasets (2020)
  6. Marina Knight, Kathryn Leeming, Guy Nason, Matthew Nunes: Generalized Network Autoregressive Processes and the GNAR Package (2020) not zbMATH
  7. Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
  8. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  9. Daniel Turek, Mark Risser: Bayesian nonstationary Gaussian process modeling: the BayesNSGP package for R (2019) arXiv
  10. 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)
  11. Morris, Samuel A.; Reich, Brian J.; Thibaud, Emeric: Exploration and inference in spatial extremes using empirical basis functions (2019)
  12. 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)
  13. 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)
  14. Robert Geitner and Robby Fritzsch and Jürgen Popp and Thomas Bocklitz: corr2D: Implementation of Two-Dimensional Correlation Analysis in R (2019) not zbMATH
  15. 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
  16. Bernardi, Mara S.; Carey, Michelle; Ramsay, James O.; Sangalli, Laura M.: Modeling spatial anisotropy via regression with partial differential regularization (2018)
  17. David Bolin; Finn Lindgren: Calculating Probabilistic Excursion Sets and Related Quantities Using excursions (2018) not zbMATH
  18. Dombry, Clément; Ribatet, Mathieu; Stoev, Stilian: Probabilities of concurrent extremes (2018)
  19. Micheas, Athanasios C.; Chen, Jiaxun: sppmix: Poisson point process modeling using normal mixture models (2018)
  20. Benjamin Taylor and Barry Rowlingson: spatsurv: An R Package for Bayesian Inference with Spatial Survival Models (2017) not zbMATH

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