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=””>doi:10.1111/j.1467-9868.2007.00633.x</a>&gt;.

References in zbMATH (referenced in 90 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. Bakar, K. Shuvo: Interpolation of daily rainfall data using censored Bayesian spatially varying model (2020)
  3. Bao, Jing Yu; Ye, Fei; Yang, Ying: Screening effect in isotropic Gaussian processes (2020)
  4. Barzegar, Zahra; Rivaz, Firoozeh: A scalable Bayesian nonparametric model for large spatio-temporal data (2020)
  5. Chu, Liu; Shi, Jiajia; Souza de Cursi, Eduardo; Ben, Shujun: Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems (2020)
  6. Heaton, Matthew J.; Berrett, Candace; Pugh, Sierra; Evans, Amber; Sloan, Chantel: Modeling bronchiolitis incidence proportions in the presence of spatio-temporal uncertainty (2020)
  7. Li, Yang; Zhu, Zhengyuan: Spatio-temporal modeling of global ozone data using convolution (2020)
  8. Murakami, Daisuke; Griffith, Daniel A.: A memory-free spatial additive mixed modeling for big spatial data (2020)
  9. Stough, T.; Cressie, N.; Kang, E. L.; Michalak, A. M.; Sahr, K.: Spatial analysis and visualization of global data on multi-resolution hexagonal grids (2020)
  10. Zammit-Mangion, Andrew; Rougier, Jonathan: Multi-scale process modelling and distributed computation for spatial data (2020)
  11. Fryer, Daniel; Olenko, Andriy: Spherical data handling and analysis with R package rcosmo (2019)
  12. Guhaniyogi, Rajarshi; Banerjee, Sudipto: Multivariate spatial meta kriging (2019)
  13. 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)
  14. Hosseinpouri, Mahdi; Khaledi, Majid Jafari: An area-specific stick breaking process for spatial data (2019)
  15. Liang, Waley W. J.; Lee, Herbert K. H.: Bayesian nonstationary Gaussian process models via treed process convolutions (2019)
  16. Li, Miaoqi; Kang, Emily L.: Randomized algorithms of maximum likelihood estimation with spatial autoregressive models for large-scale networks (2019)
  17. Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.: Likelihood approximation with hierarchical matrices for large spatial datasets (2019)
  18. Pugh, Sierra; Heaton, Matthew J.; Svedin, Jeff; Hansen, Neil: Spatiotemporal lagged models for variable rate irrigation in agriculture (2019)
  19. 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)
  20. Wang, Bo; Zhang, Qiong; Xie, Wei: Bayesian sequential data collection for stochastic simulation calibration (2019)

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