R package spNNGP: Spatial Regression Models for Large Datasets using Nearest Neighbor Gaussian Processes. Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) <doi:10.1080/10618600.2018.1537924> and Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.

References in zbMATH (referenced in 8 articles , 1 standard article )

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  1. Coube-Sisqueille, Sébastien; Liquet, Benoît: Improving performances of MCMC for nearest neighbor Gaussian process models with full data augmentation (2022)
  2. Paige, John; Fuglstad, Geir-Arne; Riebler, Andrea; Wakefield, Jon: Bayesian multiresolution modeling of georeferenced data: an extension of `LatticeKrig’ (2022)
  3. Jeffrey W. Doser, Andrew O. Finley, Marc Kery, Elise F. Zipkin: spOccupancy: An R package for single species, multispecies, and integrated spatial occupancy models (2021) arXiv
  4. Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
  5. Risser, Mark D.; Turek, Daniel: Bayesian inference for high-dimensional nonstationary Gaussian processes (2020)
  6. Finley, Andrew O.; Datta, Abhirup; Cook, Bruce D.; Morton, Douglas C.; Andersen, Hans E.; Banerjee, Sudipto: Efficient algorithms for Bayesian nearest neighbor Gaussian processes (2019)
  7. 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)
  8. Zhang, Lu; Datta, Abhirup; Banerjee, Sudipto: Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments (2019)