lgcp

lgcp: An R Package for Inference with Spatio-Temporal Log-Gaussian Cox Processes. This paper introduces an R package for spatio-temporal prediction and forecasting for log-Gaussian Cox processes. The main computational tool for these models is Markov chain Monte Carlo and the new package, lgcp, therefore also provides an extensible suite of functions for implementing MCMC algorithms for processes of this type. The modelling framework and details of inferential procedures are first presented before a tour of lgcp functionality is given via a walk-through data-analysis. Topics covered include reading in and converting data, estimation of the key components and parameters of the model, specifying output and simulation quantities, computation of Monte Carlo expectations, post-processing and simulation of data sets.


References in zbMATH (referenced in 14 articles , 2 standard articles )

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  1. Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon: Continuous Time Individual-Level Models of Infectious Disease: a Package EpiILMCT (2020) arXiv
  2. Gamerman, Dani: Spatiotemporal point processes: regression, model specifications and future directions (2019)
  3. Gonçalves, Flávio B.; Gamerman, Dani: Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2018)
  4. Micheas, Athanasios C.; Chen, Jiaxun: sppmix: Poisson point process modeling using normal mixture models (2018)
  5. Benjamin Taylor and Barry Rowlingson: spatsurv: An R Package for Bayesian Inference with Spatial Survival Models (2017) not zbMATH
  6. Altieri, L.; Cocchi, D.; Greco, F.; Illian, J. B.; Scott, E. M.: Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes (2016)
  7. Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle: Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R (2015) not zbMATH
  8. Edzer Pebesma; Roger Bivand; Paulo Ribeiro: Software for Spatial Statistics (2015) not zbMATH
  9. Patrick Brown: Model-Based Geostatistics the Easy Way (2015) not zbMATH
  10. Taylor, Benjamin M.; Diggle, Peter J.: INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes (2014)
  11. Diggle, Peter J.; Moraga, Paula; Rowlingson, Barry; Taylor, Benjamin M.: Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm (2013)
  12. Edith Gabriel; Barry Rowlingson; Peter Diggle: stpp: An R Package for Plotting, Simulating and Analyzing Spatio-Temporal Point Patterns (2013) not zbMATH
  13. Tilman Davies; David Bryant: On Circulant Embedding for Gaussian Random Fields in R (2013) not zbMATH
  14. Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle: lgcp An R Package for Inference with Spatio-Temporal Log-Gaussian Cox Processes (2011) arXiv