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 119 articles , 1 standard article )

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  1. Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree, Daniel J. Isaak, Kerrie Mengersen, Erin E. Peterson: SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks (2022) arXiv
  2. He, Linglin; Hung, Ying: Gaussian process prediction using design-based subsampling (2022)
  3. Alegría, Alfredo; Bissiri, Pier Giovanni; Cleanthous, Galatia; Porcu, Emilio; White, Philip: Multivariate isotropic random fields on spheres: nonparametric Bayesian modeling and (L^p) fast approximations (2021)
  4. Bradley, Jonathan R.: An approach to incorporate subsampling into a generic Bayesian hierarchical model (2021)
  5. Hector, Emily C.; Song, Peter X.-K.: A distributed and integrated method of moments for high-dimensional correlated data analysis (2021)
  6. Hrafnkelsson, Birgir; Siegert, Stefan; Huser, Raphaël; Bakka, Haakon; Jóhannesson, Árni V.: Max-and-smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models (2021)
  7. Hristopulos, Dionissios T.; Pavlides, Andrew; Agou, Vasiliki D.; Gkafa, Panagiota: Stochastic local interaction model: an alternative to kriging for massive datasets (2021)
  8. Hsu, Nan-Jung; Huang, Hsin-Cheng; Tsay, Ruey S.: Matrix autoregressive spatio-temporal models (2021)
  9. Huang, Da; Yao, Qiwei; Zhang, Rongmao: Krigings over space and time based on latent low-dimensional structures (2021)
  10. Katzfuss, Matthias; Guinness, Joseph: A general framework for Vecchia approximations of Gaussian processes (2021)
  11. Krock, Mitchell; Kleiber, William; Becker, Stephen: Nonstationary modeling with sparsity for spatial data via the basis graphical Lasso (2021)
  12. Lin, Luyao; Bingham, Derek; Broekgaarden, Floor; Mandel, Ilya: Uncertainty quantification of a computer model for binary black hole formation (2021)
  13. Parker, Paul A.; Holan, Scott H.; Wills, Skye A.: A general Bayesian model for heteroskedastic data with fully conjugate full-conditional distributions (2021)
  14. Pebesma, Edzer: Book review of: C. K. Wikle et al., Spatio-temporal statistics with R. Chapman and Hall/CRC (2021)
  15. Wang, Craig; Furrer, Reinhard: Combining heterogeneous spatial datasets with process-based spatial fusion models: a unifying framework (2021)
  16. Yaqiong Wang, Francesco Finazzi, Alessandro Fasso: D-STEM v2: A Software for Modeling Functional Spatio-Temporal Data (2021) not zbMATH
  17. Zhang, Tianqi; Zhang, Qiong: On the interface between nested designs and the multi-step interpolator (2021)
  18. Zhang, Tonglin: Iteratively reweighted least squares with random effects for maximum likelihood in generalized linear mixed effects models (2021)
  19. Zilber, Daniel; Katzfuss, Matthias: Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data (2021)
  20. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv

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