{ "_id": "6a1ef060b401979e73413034", "Package": "gmGeostats", "Version": "0.11.4", "Date": "2025-08-20", "Title": "Geostatistics for Compositional Analysis", "Authors@R": "c(person(given = \"Raimon\", \nfamily = \"Tolosana-Delgado\",\nrole = c(\"aut\"),\nemail = \"r.tolosana@hzdr.de\",\ncomment = c(ORCID = \"0000-0001-9847-0462\")),\nperson(given = \"Ute\",\nfamily = \"Mueller\",\nrole = c(\"aut\"),\nemail = \"u.mueller@ecu.edu.au\"),\nperson(given = \"K. Gerald\",\nfamily = \"van den Boogaart\",\nrole = c(\"ctb\", \"cre\"),\nemail = \"support@boogaart.de\"),\nperson(given = \"Hassan\",\nfamily = \"Talebi\",\nrole = c(\"ctb\", \"cph\"),\nemail = \"hassan.talebi@csiro.au\"),\nperson(given = \"Helmholtz-Zentrum Dresden-Rossendorf\", role = \"cph\"),\nperson(given = \"Edith Cowan University\", role = \"cph\")\n)", "Description": "Support for geostatistical analysis of multivariate data,\nin particular data with restrictions, e.g. positive amounts,\ncompositions, distributional data, microstructural data, etc.\nIt includes descriptive analysis and modelling for such data,\nboth from a two-point Gaussian perspective and multipoint\nperspective. The methods mainly follow Tolosana-Delgado,\nMueller and van den Boogaart (2018)\n.", "License": "CC BY-SA 4.0 | GPL (>= 2)", "URL": "https://codebase.helmholtz.cloud/geomet/gmGeostats", "Copyright": "(C) 2020 by Helmholtz-Zentrum Dresden-Rossendorf and Edith\nCowan University; gsi.DS code by Hassan Talebi", "RoxygenNote": "7.1.2", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "LazyData": "true", "Collate": "'Anamorphosis.R' 'preparations.R' 'gstatCompatibility.R'\n'gmAnisotropy.R' 'compositionsCompatibility.R' 'variograms.R'\n'gmSpatialMethodParameters.R' 'abstractClasses.R' 'accuracy.R'\n'closeup.R' 'data.R' 'exploratools.R' 'genDiag.R' 'geostats.R'\n'gmDataFrameStack.R' 'gmSimulation.R' 'gmSpatialModel.R'\n'gmValidationStrategy.R' 'grids.R' 'mask.R' 'spSupport.R'\n'uncorrelationTest.R' 'zzz.R'", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-19 08:08:44 UTC", "User": "root" }, "Author": "Raimon Tolosana-Delgado [aut] (ORCID:\n), Ute Mueller [aut], K.\nGerald van den Boogaart [ctb, cre], Hassan Talebi [ctb, cph],\nHelmholtz-Zentrum Dresden-Rossendorf [cph], Edith Cowan\nUniversity [cph]", "Maintainer": "K. 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"Internal function, conditional turning bands realisations", "topics": [ "gsi.CondTurningBands" ] }, { "page": "gsi.DS", "title": "Workhorse function for direct sampling", "topics": [ "gsi.DS" ] }, { "page": "gsi.EVario2D", "title": "Empirical variogram or covariance function in 2D", "concept": [ "gmEVario functions" ], "topics": [ "gsi.EVario2D" ] }, { "page": "gsi.EVario3D", "title": "Empirical variogram or covariance function in 3D", "concept": [ "gmEVario functions" ], "topics": [ "gsi.EVario3D" ] }, { "page": "gsi.gstatCokriging2compo", "title": "Reorganisation of cokriged compositions", "topics": [ "gsi.gstatCokriging2compo", "gsi.gstatCokriging2compo.data.frame", "gsi.gstatCokriging2compo.default", "gsi.gstatCokriging2rmult", "gsi.gstatCokriging2rmult.data.frame", "gsi.gstatCokriging2rmult.default" ] }, { "page": "gsi.orig", "title": "extract information about the original data, if available", "topics": [ "gsi.getV", "gsi.orig" ] }, { "page": "gsi.produceV", "title": "Create a matrix of logcontrasts and name prefix", "topics": [ "gsi.produceV" ] }, { "page": "gsi.TurningBands", "title": "Internal function, unconditional turning bands realisations", "topics": [ "gsi.TurningBands" ] }, { "page": "has.missings.data.frame", "title": "Check presence of missings check presence of missings in a data.frame", "topics": [ "has.missings.data.frame" ] }, { "page": "image_cokriged", "title": "Plot an image of gridded data", "topics": [ "image_cokriged", "image_cokriged.default", "image_cokriged.spatialGridAcomp", "image_cokriged.spatialGridRmult" ] }, { "page": "image.logratioVariogramAnisotropy", "title": "Plot variogram maps for anisotropic logratio variograms", "topics": [ "image.logratioVariogramAnisotropy" ] }, { "page": "image.mask", "title": "Image method for mask objects", "topics": [ "image.mask" ] }, { "page": "is.anisotropySpecification", "title": "Check for any anisotropy class", "concept": [ "anisotropy" ], "topics": [ "is.anisotropySpecification" ] }, { "page": "is.isotropic", "title": "Check for anisotropy of a theoretical variogram", "topics": [ "is.isotropic" ] }, { "page": "KrigingNeighbourhood", "title": "Create a parameter set of local for neighbourhood specification.", "topics": [ "KrigingNeighbourhood" ] }, { "page": "LeaveOneOut", "title": "Specify the leave-one-out strategy for validation of a spatial model", "concept": [ "validation functions" ], "topics": [ "LeaveOneOut" ] }, { "page": "length.gmCgram", "title": "Length, and number of columns or rows", "concept": [ "gmCgram functions" ], "topics": [ "length.gmCgram", "ncol.gmCgram", "nrow.gmCgram" ] }, { "page": "LMCAnisCompo", "title": "Create a anisotropic model for regionalized compositions", "topics": [ "LMCAnisCompo" ] }, { "page": "logratioVariogram", "title": "Empirical logratio variogram calculation", "topics": [ "logratioVariogram" ] }, { "page": "variogram_gmSpatialModel", "title": "Variogram method for gmSpatialModel objects", "topics": [ "logratioVariogram_gmSpatialModel", "variogram_gmSpatialModel" ] }, { "page": "logratioVariogram-acomp-method", "title": "Logratio variogram of a compositional data", "topics": [ "logratioVariogram,acomp-method" ] }, { "page": "Maf.data.frame", "title": "Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogram", "concept": [ "generalised Diagonalisations" ], "topics": [ "genDiag", "Maf", "Maf.acomp", "Maf.aplus", "Maf.ccomp", "Maf.data.frame", "Maf.rcomp", "Maf.rmult", "Maf.rplus", "RJD", "RJD.acomp", "RJD.default", "RJD.rcomp", "UWEDGE", "UWEDGE.acomp", "UWEDGE.default", "UWEDGE.rcomp" ] }, { "page": "make.gmCompositionalGaussianSpatialModel", "title": "Construct a Gaussian gmSpatialModel for regionalized compositions", "concept": [ "gmSpatialModel" ], "topics": [ "make.gmCompositionalGaussianSpatialModel" ] }, { "page": "make.gmCompositionalMPSSpatialModel", "title": "Construct a Multi-Point gmSpatialModel for regionalized compositions", "concept": [ "gmSpatialModel" ], "topics": [ "make.gmCompositionalMPSSpatialModel" ] }, { "page": "make.gmMultivariateGaussianSpatialModel", "title": "Construct a Gaussian gmSpatialModel for regionalized multivariate data", "concept": [ "gmSpatialModel" ], "topics": [ "make.gmMultivariateGaussianSpatialModel" ] }, { "page": "mean.accuracy", "title": "Mean accuracy", "concept": [ "accuracy functions" ], "topics": [ "mean.accuracy" ] }, { "page": "mean.spatialDecorrelationMeasure", "title": "Average measures of spatial decorrelation", "topics": [ "mean.spatialDecorrelationMeasure" ] }, { "page": "ModelStructuralFunctionSpecification-class", "title": "Structural function model specification", "topics": [ "ModelStructuralFunctionSpecification-class" ] }, { "page": "ndirections", "title": "Number of directions of an empirical variogram", "concept": [ "gmCgram functions", "gmEVario functions" ], "topics": [ "ndirections", "ndirections.azimuth", "ndirections.azimuthInterval", "ndirections.default", "ndirections.gmEVario", "ndirections.gstatVariogram", "ndirections.logratioVariogram", "ndirections.logratioVariogramAnisotropy" ] }, { "page": "NfoldCrossValidation", "title": "Specify a strategy for validation of a spatial model", "concept": [ "validation functions" ], "topics": [ "NfoldCrossValidation" ] }, { "page": "NGSAustralia", "title": "National Geochemical Survey of Australia: soil data", "topics": [ "NGSAustralia" ] }, { "page": "noSpatCorr.test", "title": "Test for lack of spatial correlation", "topics": [ "noSpatCorr.test", "noSpatCorr.test.data.frame", "noSpatCorr.test.default", "noSpatCorr.test.matrix" ] }, { "page": "pairsmap", "title": "Multiple maps Matrix of maps showing different combinations of components of a composition, user defined", "topics": [ "pairsmap", "pairsmap.default", "pairsmap.SpatialPointsDataFrame" ] }, { "page": "plot.accuracy", "title": "Plot method for accuracy curves", "concept": [ "accuracy functions" ], "topics": [ "plot.accuracy" ] }, { "page": "plot.gmCgram", "title": "Draw cuves for covariance/variogram models", "concept": [ "gmCgram functions" ], "topics": [ "plot.gmCgram" ] }, { "page": "plot.gmEVario", "title": "Plot empirical variograms", "concept": [ "gmEVario functions" ], "topics": [ "plot.gmEVario" ] }, { "page": "plot.logratioVariogramAnisotropy", "title": "Plot variogram lines of empirical directional logratio variograms", "topics": [ "plot.logratioVariogramAnisotropy" ] }, { "page": "plot.swarmPlot", "title": "Plotting method for swarmPlot objects", "topics": [ "plot.swarmPlot" ] }, { "page": "precision", "title": "Precision calculations", "concept": [ "accuracy functions" ], "topics": [ "precision", "precision.accuracy" ] }, { "page": "predict_gmSpatialModel", "title": "Predict method for objects of class 'gmSpatialModel'", "concept": [ "gmSpatialModel" ], "topics": [ "Predict", "predict", "Predict,gmSpatialModel,ANY,ANY-method", "Predict,gmSpatialModel,ANY,gmCholeskyDecomposition-method", "Predict,gmSpatialModel,ANY,gmDirectSamplingParameters-method", "Predict,gmSpatialModel,ANY,gmNeighbourhoodSpecification-method", "Predict,gmSpatialModel,ANY,gmSequentialSimulation-method", "Predict,gmSpatialModel,ANY,gmTurningBands-method", "predict,gmSpatialModel-method", "predict.gmSpatialModel", "predict_gmSpatialModel" ] }, { "page": "predict.genDiag", "title": "Predict method for generalised diagonalisation objects", "concept": [ "generalised Diagonalisations" ], "topics": [ "predict.genDiag" ] }, { "page": "predict.LMCAnisCompo", "title": "Compute model variogram values Evaluate the variogram model provided at some lag vectors", "topics": [ "predict.LMCAnisCompo" ] }, { "page": "print.mask", "title": "Print method for mask objects", "concept": [ "masking functions" ], "topics": [ "print.mask" ] }, { "page": "pwlrmap", "title": "Compositional maps, pairwise logratios Matrix of maps showing different combinations of components of a composition, in pairwise logratios", "topics": [ "pwlrmap" ] }, { "page": "SequentialSimulation", "title": "Create a parameter set specifying a gaussian sequential simulation algorithm", "topics": [ "SequentialSimulation" ] }, { "page": "setCgram", "title": "Generate D-variate variogram models", "concept": [ "gmCgram" ], "topics": [ "gsi.validModels", "setCgram", "vg.Exp", "vg.exp", "vg.Exponential", "vg.Gau", "vg.Gauss", "vg.gauss", "vg.Sph", "vg.sph", "vg.Spherical" ] }, { "page": "setGridOrder", "title": "Set or get the ordering of a grid", "topics": [ "getGridOrder", "gridOrder_array", "gridOrder_GSLib", "gridOrder_gstat", "gridOrder_sp", "setGridOrder", "setGridOrder_array", "setGridOrder_sp" ] }, { "page": "setMask", "title": "Set a mask on an object", "concept": [ "masking functions" ], "topics": [ "setMask", "setMask.data.frame", "setMask.DataFrameStack", "setMask.default", "setMask.GridTopology", "setMask.SpatialGrid", "setMask.SpatialPoints" ] }, { "page": 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