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 100 articles , 1 standard article )

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  1. 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)
  2. Hector, Emily C.; Song, Peter X.-K.: A distributed and integrated method of moments for high-dimensional correlated data analysis (2021)
  3. Huang, Da; Yao, Qiwei; Zhang, Rongmao: Krigings over space and time based on latent low-dimensional structures (2021)
  4. Katzfuss, Matthias; Guinness, Joseph: A general framework for Vecchia approximations of Gaussian processes (2021)
  5. Yaqiong Wang, Francesco Finazzi, Alessandro Fasso: D-STEM v2: A Software for Modeling Functional Spatio-Temporal Data (2021) not zbMATH
  6. Zilber, Daniel; Katzfuss, Matthias: Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data (2021)
  7. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  8. Bakar, K. Shuvo: Interpolation of daily rainfall data using censored Bayesian spatially varying model (2020)
  9. Bao, Jing Yu; Ye, Fei; Yang, Ying: Screening effect in isotropic Gaussian processes (2020)
  10. Barzegar, Zahra; Rivaz, Firoozeh: A scalable Bayesian nonparametric model for large spatio-temporal data (2020)
  11. Bradley, Jonathan R.; Holan, Scott H.; Wikle, Christopher K.: Bayesian hierarchical models with conjugate full-conditional distributions for dependent data from the natural exponential family (2020)
  12. Chu, Liu; Shi, Jiajia; Souza de Cursi, Eduardo; Ben, Shujun: Efficiency improvement of kriging surrogate model by subset simulation in implicit expression problems (2020)
  13. Edwards, Matthew; Castruccio, Stefano; Hammerling, Dorit: Marginally parameterized spatio-temporal models and stepwise maximum likelihood estimation (2020)
  14. Heaton, Matthew J.; Berrett, Candace; Pugh, Sierra; Evans, Amber; Sloan, Chantel: Modeling bronchiolitis incidence proportions in the presence of spatio-temporal uncertainty (2020)
  15. Li, Yang; Zhu, Zhengyuan: Spatio-temporal modeling of global ozone data using convolution (2020)
  16. Murakami, Daisuke; Griffith, Daniel A.: A memory-free spatial additive mixed modeling for big spatial data (2020)
  17. 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)
  18. Tajbakhsh, Sam Davanloo; Aybat, Necdet Serhat; Del Castillo, Enrique: On the theoretical guarantees for parameter estimation of Gaussian random field models: a sparse precision matrix approach (2020)
  19. Zammit-Mangion, Andrew; Rougier, Jonathan: Multi-scale process modelling and distributed computation for spatial data (2020)
  20. Zhang, Bohai; Cressie, Noel: Bayesian inference of spatio-temporal changes of arctic sea ice (2020)

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