FRK

R package FRK. Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach, discussed in Cressie and Johannesson (2008), decomposes the field, and hence the covariance function, using a fixed set of n basis functions, where n is typically much smaller than the number of data points (or polygons) m. The method naturally allows for non-stationary, anisotropic covariance functions and the use of observations with varying support (with known error variance). The projected field is a key building block of the Spatial Random Effects (SRE) model, on which this package is based. The package FRK provides helper functions to model, fit, and predict using an SRE with relative ease. Reference: Cressie, N. and Johannesson, G. (2008) &lt;<a href=”http://dx.doi.org/10.1111/j.1467-9868.2007.00633.x”>doi:10.1111/j.1467-9868.2007.00633.x</a>&gt;.


References in zbMATH (referenced in 81 articles , 1 standard article )

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  1. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  2. Chu, Liu; Shi, Jiajia; Souza de Cursi, Eduardo; Ben, Shujun: Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems (2020)
  3. Guhaniyogi, Rajarshi; Banerjee, Sudipto: Multivariate spatial meta kriging (2019)
  4. Heaton, Matthew J.; Datta, Abhirup; Finley, Andrew O.; Furrer, Reinhard; Guinness, Joseph; Guhaniyogi, Rajarshi; Gerber, Florian; Gramacy, Robert B.; Hammerling, Dorit; Katzfuss, Matthias; Lindgren, Finn; Nychka, Douglas W.; Sun, Furong; Zammit-Mangion, Andrew: A case study competition among methods for analyzing large spatial data (2019)
  5. Hosseinpouri, Mahdi; Khaledi, Majid Jafari: An area-specific stick breaking process for spatial data (2019)
  6. Liang, Waley W. J.; Lee, Herbert K. H.: Bayesian nonstationary Gaussian process models via treed process convolutions (2019)
  7. Li, Miaoqi; Kang, Emily L.: Randomized algorithms of maximum likelihood estimation with spatial autoregressive models for large-scale networks (2019)
  8. Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.: Likelihood approximation with hierarchical matrices for large spatial datasets (2019)
  9. Pugh, Sierra; Heaton, Matthew J.; Svedin, Jeff; Hansen, Neil: Spatiotemporal lagged models for variable rate irrigation in agriculture (2019)
  10. Tipton, John R.; Hooten, Mevin B.; Nolan, Connor; Booth, Robert K.; McLachlan, Jason: Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction (2019)
  11. Wang, Bo; Zhang, Qiong; Xie, Wei: Bayesian sequential data collection for stochastic simulation calibration (2019)
  12. White, Philip; Porcu, Emilio: Towards a complete picture of stationary covariance functions on spheres cross time (2019)
  13. Bivand, Roger; Krivoruchko, Konstantin: Big data sampling and spatial analysis: “which of the two ladles, of fig-wood or gold, is appropriate to the soup and the pot?” (2018)
  14. Bradley, Jonathan R.; Holan, Scott H.; Wikle, Christopher K.: Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion) (2018)
  15. Castruccio, Stefano; Genton, Marc G.: Principles for statistical inference on big spatio-temporal data from climate models (2018)
  16. Cressie, Noel: Mission (\mathrmCO_2)ntrol: a statistical scientist’s role in remote sensing of atmospheric carbon dioxide (2018)
  17. Cressie, Noel: Rejoinder (2018)
  18. Fan, Minjie; Paul, Debashis; Lee, Thomas C. M.; Matsuo, Tomoko: A multi-resolution model for non-Gaussian random fields on a sphere with application to ionospheric electrostatic potentials (2018)
  19. Genton, Marc G.; Jeong, Jaehong: Comment to “Mission (\mathrmCO_2)ntrol: a statistical scientist’s role in remote sensing of atmospheric carbon dioxide” (2018)
  20. Hardouin, Cécile; Cressie, Noel: Two-scale spatial models for binary data (2018)

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