{ "_id": "6a1eae73b25058d4daada746", "Package": "GeoModels", "Version": "2.1.0", "Date": "2025-01-05", "Authors@R": "c(person(\"Moreno\", \"Bevilacqua\", role = c(\"aut\", \"cre\",\"cph\"),\nemail = \"moreno.bevilacqua89@gmail.com\"),\nperson(\"Víctor\", \"Morales-Oñate\", role = \"ctb\"),\nperson(\"Francisco\", \"Cuevas-Pacheco\", role = \"ctb\"),\nperson(\"Christian\", \"Caamaño-Carrillo\", role = \"ctb\"))", "Description": "Functions for Gaussian and Non Gaussian (bivariate)\nspatial and spatio-temporal data analysis are provided for a)\n(fast) simulation of random fields, b) inference for random\nfields using standard likelihood and a likelihood approximation\nmethod called weighted composite likelihood based on pairs and\nb) prediction using (local) best linear unbiased prediction.\nWeighted composite likelihood can be very efficient for\nestimating massive datasets. Both regression and spatial\n(temporal) dependence analysis can be jointly performed.\nFlexible covariance models for spatial and spatial-temporal\ndata on Euclidean domains and spheres are provided. There are\nalso many useful functions for plotting and performing\ndiagnostic analysis. Different non Gaussian random fields can\nbe considered in the analysis. Among them, random fields with\nmarginal distributions such as Skew-Gaussian, Student-t,\nTukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian,\nBinomial, Negative Binomial and Poisson. See the URL for the\npapers associated with this package, as for instance,\nBevilacqua and Gaetan (2015) ,\nBevilacqua et al. (2016) ,\nVallejos et al. (2020) ,\nBevilacqua et. al (2020) , Bevilacqua et.\nal (2021) , Bevilacqua et al. (2022)\n, Morales-Navarrete et al.\n(2023) , and a large class\nof examples and tutorials.", "Title": "Procedures for Gaussian and Non Gaussian Geostatistical (Large)\nData Analysis", "License": "GPL (>= 3)", "Encoding": "UTF-8", "URL": "https://vmoprojs.github.io/GeoModels-page/", "BugReports": "https://github.com/vmoprojs/GeoModels/issues", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-02 10:10:48 UTC", "User": "root" }, "Author": "Moreno Bevilacqua [aut, cre, cph], Víctor Morales-Oñate [ctb],\nFrancisco Cuevas-Pacheco [ctb], Christian Caamaño-Carrillo\n[ctb]", "Maintainer": "Moreno Bevilacqua ", "Config/pak/sysreqs": "make pari-gp", "Repository": "https://vmoprojs.r-universe.dev", "Date/Publication": "2025-01-27 08:34:45 UTC", "RemoteUrl": "https://github.com/vmoprojs/geomodels", "RemoteRef": "HEAD", "RemoteSha": "97e997ea1eb53b6381a9a96a5906ab3381c2c274", "MD5sum": "631958b41d5adc7ae481a2df1d3df710", "_user": "vmoprojs", "_type": "src", "_file": 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Input", "topics": [ "CkInput" ] }, { "page": "CkLikelihood", "title": "Checking Composite-likelihood Type", "topics": [ "CkLikelihood" ] }, { "page": "CkModel", "title": "Checking Random Field type", "topics": [ "CkModel" ] }, { "page": "CkType", "title": "Checking Likelihood Objects", "topics": [ "CkType" ] }, { "page": "CompIndLik2", "title": "Optimizes the Composite indipendence log-likelihood", "topics": [ "CompIndLik2" ] }, { "page": "CompLik", "title": "Optimizes the Composite log-likelihood", "topics": [ "CompLik" ] }, { "page": "CompLik2", "title": "Optimizes the Composite log-likelihood", "topics": [ "CompLik2" ] }, { "page": "CorrelationPar", "title": "Lists the Parameters of a Correlation Model", "topics": [ "CorrelationPar" ] }, { "page": "CorrParam", "title": "Lists the Parameters of a Correlation Model", "topics": [ "CorrParam" ] }, { "page": "GeoAniso", "title": "Spatial Anisotropy correction", "topics": [ "GeoAniso" ] }, { "page": "GeoCorrFct", "title": "Spatial and 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"GeoNeighIndex", "title": "Spatial or spatiotemporal near neighbour indices.", "topics": [ "GeoNeighIndex" ] }, { "page": "GeoNosymindices", "title": "GeoNosymindices.", "topics": [ "GeoNosymindices" ] }, { "page": "GeoOutlier", "title": "Spatio (temporal) outliers detection", "topics": [ "GeoOutlier" ] }, { "page": "GeoPit", "title": "Probability integral or normal score tranformation", "topics": [ "GeoPit" ] }, { "page": "GeoQQ", "title": "Quantile-quantile plot", "topics": [ "GeoQQ" ] }, { "page": "GeoResiduals", "title": "Computes fitted covariance and/or variogram", "topics": [ "GeoResiduals" ] }, { "page": "GeoScatterplot", "title": "h-scatterplot for space and space-time data.", "topics": [ "GeoScatterplot" ] }, { "page": "GeoScores", "title": "Computation of predictive scores", "topics": [ "GeoScores" ] }, { "page": "GeoSim", "title": "Simulation of Gaussian and non Gaussian Random Fields.", "topics": [ "GeoSim", "print.GeoSim" ] }, { "page": "GeoSimapprox", "title": "Fast 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