R package. CompRandFld: Composite-Likelihood Based Analysis of Random Fields. A set of procedures for the analysis of Random Fields using likelihood and non-standard likelihood methods is provided. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood inference is emerging as a useful tool for mitigating such computational problems. This methodology shows satisfactory results when compared with other techniques such as the tapering method. Moreover, composite likelihood (and related quantities) have some useful properties similar to those of the standard likelihood.
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- RESSTE Network; et al.: Analyzing spatio-temporal data with R: everything you always wanted to know -- but were afraid to ask (2017)
- Bevilacqua, M.; Fassò, A.; Gaetan, C.; Porcu, E.; Velandia, D.: Covariance tapering for multivariate Gaussian random fields estimation (2016)
- Bevilacqua, Moreno; Alegria, Alfredo; Velandia, Daira; Porcu, Emilio: Composite likelihood inference for multivariate Gaussian random fields (2016)
- Bevilacqua, Moreno; Gaetan, Carlo: Comparing composite likelihood methods based on pairs for spatial Gaussian random fields (2015)
- Edzer Pebesma; Roger Bivand; Paulo Ribeiro: Software for Spatial Statistics (2015) not zbMATH
- Martin Schlather; Alexander Malinowski; Peter Menck; Marco Oesting; Kirstin Strokorb: Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields (2015) not zbMATH
- Simone Padoan; Moreno Bevilacqua: Analysis of Random Fields Using CompRandFld (2015) not zbMATH
- Bacro, Jean-Noel; Gaetan, Carlo: Estimation of spatial max-stable models using threshold exceedances (2014)
- Fawcett, Lee; Walshaw, David: Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach (2014)