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 68 articles , 1 standard article )

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  1. Guhaniyogi, Rajarshi; Banerjee, Sudipto: Multivariate spatial meta kriging (2019)
  2. Wang, Bo; Zhang, Qiong; Xie, Wei: Bayesian sequential data collection for stochastic simulation calibration (2019)
  3. 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)
  4. Bradley, Jonathan R.; Holan, Scott H.; Wikle, Christopher K.: Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion) (2018)
  5. Castruccio, Stefano; Genton, Marc G.: Principles for statistical inference on big spatio-temporal data from climate models (2018)
  6. Cressie, Noel: Rejoinder (2018)
  7. Cressie, Noel: Mission (\mathrmCO_2)ntrol: a statistical scientist’s role in remote sensing of atmospheric carbon dioxide (2018)
  8. 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)
  9. Genton, Marc G.; Jeong, Jaehong: Comment to “Mission (\mathrmCO_2)ntrol: a statistical scientist’s role in remote sensing of atmospheric carbon dioxide” (2018)
  10. Hardouin, Cécile; Cressie, Noel: Two-scale spatial models for binary data (2018)
  11. Linero, Antonio R.; Bradley, Jonathan R.; Desai, Apurva: Multi-rubric models for ordinal spatial data with application to online ratings data (2018)
  12. Marchetti, Yuliya; Nguyen, Hai; Braverman, Amy; Cressie, Noel: Spatial data compression via adaptive dispersion clustering (2018)
  13. Suesse, Thomas: Estimation of spatial autoregressive models with measurement error for large data sets (2018)
  14. Zammit-Mangion, Andrew; Rougier, Jonathan: A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields (2018)
  15. Zhou, Haiming; Hanson, Timothy: A unified framework for Fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially referenced data (2018)
  16. Andrew Zammit-Mangion, Noel Cressie: FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (2017) arXiv
  17. Bachoc, François; Contal, Emile; Maatouk, Hassan; Rullière, Didier: Gaussian processes for computer experiments (2017)
  18. Bass, Mark R.; Sahu, Sujit K.: A comparison of centring parameterisations of Gaussian process-based models for Bayesian computation using MCMC (2017)
  19. Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.: Spherical process models for global spatial statistics (2017)
  20. Katzfuss, Matthias; Hammerling, Dorit: Parallel inference for massive distributed spatial data using low-rank models (2017)

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