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 33 articles )

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  1. Bernardi, Mara S.; Carey, Michelle; Ramsay, James O.; Sangalli, Laura M.: Modeling spatial anisotropy via regression with partial differential regularization (2018)
  2. David Bolin; Finn Lindgren: Calculating Probabilistic Excursion Sets and Related Quantities Using excursions (2018)
  3. Benjamin Taylor and Barry Rowlingson: spatsurv: An R Package for Bayesian Inference with Spatial Survival Models (2017)
  4. Falke, Andreas; Hruschka, Harald: A Monte Carlo study of design-generating algorithms for the latent class mixed logit model (2017)
  5. Marcello Chiodi and Giada Adelfio: Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes’ Description (2017)
  6. Matthew Dixon, Diego Klabjan, Lan Wei: OSTSC: Over Sampling for Time Series Classification in R (2017) arXiv
  7. Risk, J.; Ludkovski, M.: Statistical emulators for pricing and hedging longevity risk products (2016)
  8. Robert Gramacy: laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R (2016)
  9. Shaby, Benjamin A.; Reich, Brian J.; Cooley, Daniel; Kaufman, Cari G.: A Markov-switching model for heat waves (2016)
  10. Andrew Finley; Sudipto Banerjee; Alan Gelfand: spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models (2015)
  11. Blake MacDonald; Pritam Ranjan; Hugh Chipman: GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs (2015)
  12. Fabio Sigrist; Hans Künsch; Werner Stahel: spate: An R Package for Spatio-Temporal Modeling with a Stochastic Advection-Diffusion Process (2015)
  13. Flora Jay; Olivier François; Eric Durand; Michael Blum: POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models (2015)
  14. Khandoker Bakar; Sujit Sahu: spTimer: Spatio-Temporal Bayesian Modeling Using R (2015)
  15. Mark D. Risser, Catherine A. Calder: Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R (2015) arXiv
  16. Martin Schlather; Alexander Malinowski; Peter Menck; Marco Oesting; Kirstin Strokorb: Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields (2015)
  17. Simone Padoan; Moreno Bevilacqua: Analysis of Random Fields Using CompRandFld (2015)
  18. Wu, Guohui; Holan, Scott H.; Nilon, Charles H.; Wikle, Christopher K.: Bayesian binomial mixture models for estimating abundance in ecological monitoring studies (2015)
  19. Zachary D. Weller: spTest: An R Package Implementing Nonparametric Tests of Isotropy (2015) arXiv
  20. Jay Ver Hoef; Erin Peterson; David Clifford; Rohan Shah: SSN: An R Package for Spatial Statistical Modeling on Stream Networks (2014)

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