Package: gmGeostats 0.11.3

K. Gerald van den Boogaart

gmGeostats: Geostatistics for Compositional Analysis

Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts, compositions, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. The methods mainly follow Tolosana-Delgado, Mueller and van den Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.

Authors:Raimon Tolosana-Delgado [aut], Ute Mueller [aut], K. Gerald van den Boogaart [ctb, cre], Hassan Talebi [ctb, cph], Helmholtz-Zentrum Dresden-Rossendorf [cph], Edith Cowan University [cph]

gmGeostats_0.11.3.tar.gz
gmGeostats_0.11.3.tar.gz(r-4.5-noble)gmGeostats_0.11.3.tar.gz(r-4.4-noble)
gmGeostats_0.11.3.tgz(r-4.4-emscripten)gmGeostats_0.11.3.tgz(r-4.3-emscripten)
gmGeostats.pdf |gmGeostats.html
gmGeostats/json (API)
NEWS

# Install 'gmGeostats' in R:
install.packages('gmGeostats', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • NGSAustralia - National Geochemical Survey of Australia: soil data
  • Windarling - Ore composition of a bench at a mine in Windarling, West Australia.
  • gsi.validModels - Generate D-variate variogram models
  • vg.Exp - Generate D-variate variogram models
  • vg.Exponential - Generate D-variate variogram models
  • vg.Gau - Generate D-variate variogram models
  • vg.Gauss - Generate D-variate variogram models
  • vg.Sph - Generate D-variate variogram models
  • vg.Spherical - Generate D-variate variogram models
  • vg.exp - Generate D-variate variogram models
  • vg.gauss - Generate D-variate variogram models
  • vg.sph - Generate D-variate variogram models

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openmp

3.00 score 1 stars 6 scripts 287 downloads 92 exports 36 dependencies

Last updated 2 years agofrom:63e34ff8e2. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 08 2024
R-4.5-linux-x86_64NOTEDec 08 2024

Exports:accuracyanaanaBackwardanaForwardanis_GSLIBpar2Aanis2D_par2Aanis3D_par2AAnisotropyRangeMatrixAnisotropyScalingas.AnisotropyRangeMatrixas.AnisotropyScalingas.CompLinModCoRegas.DataFrameStackas.directorVectoras.gmCgramas.gmEVarioas.gmSpatialModelas.gstatas.gstatVariogramas.LMCAnisCompoas.logratioVariogramas.logratioVariogramAnisotropyas.variogramModelCholeskyDecompositionconstructMaskDataFrameStackDSparsfit_lmcgetGridOrdergetMaskgetStackElementgetTellusgmApplygridOrder_arraygridOrder_GSLibgridOrder_gstatgridOrder_spgsi.DSgsi.EVario2Dgsi.EVario3Dgsi.gstatCokriging2compogsi.gstatCokriging2rmultgsi.produceVgsi.TurningBandsimage_cokrigedis.anisotropySpecificationis.isotropicKrigingNeighbourhoodLeaveOneOutLMCAnisCompologratioVariogramlogratioVariogram_gmSpatialModelMafmake.gmCompositionalGaussianSpatialModelmake.gmCompositionalMPSSpatialModelmake.gmMultivariateGaussianSpatialModelndirectionsNfoldCrossValidationnoSpatCorr.testnoStackDimpairsmapprecisionpredictPredictpwlrmapRJDSequentialSimulationsetCgramsetGridOrdersetGridOrder_arraysetGridOrder_spsetMasksetStackElementsortDataInGridspatialDecorrelationspatialGridAcompspatialGridRmultspectralcolorssphTransstackDimstackDim<-swarmPlotswathTurningBandsunmaskUWEDGEvalidatevariogramvariogram_gmSpatialModelvariogramModelPlotwrite.GSLibxvErrorMeasures

Dependencies:abindbayesmbootclassclassIntcodetoolscompositionsDBIDEoptimRe1071FNNforeachgstatintervalsiteratorsKernSmoothlatticemagrittrMASSproxyRColorBrewerRcppRcppArmadillorlangrobustbases2sfsftimespspacetimestarstensorAunitswkxtszoo

How to register new layer datatypes

Rendered fromregister_new_layer_datatype.Rmdusingknitr::rmarkdownon Dec 08 2024.

Last update: 2022-11-30
Started: 2021-07-15

Multivariate geostatistics with gmGeostats

Rendered fromgmGeostats.Rmdusingknitr::rmarkdownon Dec 08 2024.

Last update: 2021-07-15
Started: 2020-09-16

Readme and manuals

Help Manual

Help pageTopics
Extract rows of a DataFrameStack[.DataFrameStack
Subsetting of gmCgram variogram structures[.gmCgram
Subsetting of logratioVariogram objects[.logratioVariogramAnisotropy `[.logratioVariogram`
Subsetting of gmCgram variogram structures[[.gmCgram
Combination of gmCgram variogram structures+.gmCgram
Compute accuracy and precisionaccuracy accuracy.data.frame accuracy.DataFrameStack
Flow anamorphosis transform Compute a transformation that gaussianizes a certain data setana
Backward gaussian anamorphosis backward transformation to multivariate gaussian scoresanaBackward
Forward gaussian anamorphosis forward transformation to multivariate gaussian scoresanaForward
Produce anisotropy scaling matrix from angle and anisotropy ratiosanis2D_par2A anis3D_par2A anis_GSLIBpar2A
Force a matrix to be anisotropy range matrix,AnisotropyRangeMatrix as.AnisotropyRangeMatrix.AnisotropyScaling
Convert to anisotropy scaling matrixAnisotropyScaling
Force a matrix to be anisotropy range matrix,as.AnisotropyRangeMatrix as.AnisotropyRangeMatrix.AnisotropyRangeMatrix as.AnisotropyRangeMatrix.default
Convert to anisotropy scaling matrixas.AnisotropyScaling as.AnisotropyScaling.AnisotropyRangeMatrix as.AnisotropyScaling.AnisotropyScaling as.AnisotropyScaling.numeric
Convert a stacked data frame into an arrayas.array.DataFrameStack
Recast a model to the variogram model of package "compositions"as.CompLinModCoReg as.CompLinModCoReg.CompLinModCoReg as.CompLinModCoReg.LMCAnisCompo
Express a direction as a director vectoras.directorVector as.directorVector.azimuth as.directorVector.azimuthInterval as.directorVector.default
Convert a gmCgram object to an (evaluable) functionas.function.gmCgram predict.gmCgram
Convert theoretical structural functions to gmCgram formatas.gmCgram as.gmCgram.default as.gmCgram.LMCAnisCompo as.gmCgram.variogramModel as.gmCgram.variogramModelList
Convert empirical structural function to gmEVario formatas.gmEVario as.gmEVario.default as.gmEVario.gstatVariogram as.gmEVario.logratioVariogram as.gmEVario.logratioVariogramAnisotropy
Recast spatial object to gmSpatialModel formatas.gmSpatialModel as.gmSpatialModel.default as.gmSpatialModel.gstat
Convert a regionalized data container to gstatas.gstat as.gstat.default
Represent an empirical variogram in "gstatVariogram" formatas.gstatVariogram as.gstatVariogram.default as.gstatVariogram.gmEVario as.gstatVariogram.logratioVariogram as.gstatVariogram.logratioVariogramAnisotropy
Convert a stacked data frame into a list of data.framesas.list.DataFrameStack
Recast compositional variogram model to format LMCAnisCompoas.LMCAnisCompo as.LMCAnisCompo.CompLinModCoReg as.LMCAnisCompo.gmCgram as.LMCAnisCompo.gstat as.LMCAnisCompo.LMCAnisCompo as.LMCAnisCompo.variogramModelList gstat2LMCAnisCompo
Recast empirical variogram to format logratioVariogramas.logratioVariogram as.logratioVariogram.gmEVario as.logratioVariogram.gstatVariogram as.logratioVariogram.logratioVariogram
Convert empirical variogram to "logratioVariogramAnisotropy"as.logratioVariogramAnisotropy as.logratioVariogramAnisotropy.default as.logratioVariogramAnisotropy.logratioVariogram as.logratioVariogramAnisotropy.logratioVariogramAnisotropy
Convert an LMC variogram model to gstat formatas.variogramModel as.variogramModel.CompLinModCoReg as.variogramModel.default as.variogramModel.gmCgram as.variogramModel.LMCAnisCompo
Create a parameter set specifying a LU decomposition simulation algorithmCholeskyDecomposition
Colored biplot for gemeralised diagonalisations Colored biplot method for objects of class genDiagcoloredBiplot.genDiag
Constructs a mask for a gridconstructMask
Create a data frame stackas.DataFrameStack as.DataFrameStack.array as.DataFrameStack.data.frame as.DataFrameStack.list DataFrameStack DataFrameStack.array DataFrameStack.data.frame DataFrameStack.list
Return the dimnames of a DataFrameStackdimnames,Spatial-method dimnames.DataFrameStack
Create a parameter set specifying a direct sampling algorithmDirectSamplingParameters DSpars
Empirical structural function specificationEmpiricalStructuralFunctionSpecification-class
Fit an LMC to an empirical variogramfit_lmc fit_lmc.default fit_lmc.gstatVariogram fit_lmc.logratioVariogram fit_lmc.logratioVariogramAnisotropy
Get the mask info out of a spatial data objectgetMask getMask.default getMask.SpatialPixels getMask.SpatialPixelsDataFrame getMask.SpatialPointsDataFrame
Set or get the i-th data frame of a data.frame stackgetStackElement getStackElement.DataFrameStack getStackElement.default getStackElement.list setStackElement setStackElement.data.frame setStackElement.DataFrameStack setStackElement.default setStackElement.list
Download the Tellus survey data set (NI)getTellus
Apply Functions Over Array or DataFrameStack MarginsgmApply gmApply.DataFrameStack gmApply.default
parameters for Spatial Gaussian methods of any kindgmGaussianMethodParameters-class
parameters for Gaussian Simulation methodsgmGaussianSimulationAlgorithm-class
parameters for Multiple-Point Statistics methodsgmMPSParameters-class
Neighbourhood descriptiongmNeighbourhoodSpecification-class
Parameter specification for a spatial simulation algorithmgmSimulationAlgorithm-class
General description of a spatial data containergmSpatialDataContainer-class
Parameter specification for any spatial methodgmSpatialMethodParameters-class
Conditional spatial model data containeras.gstat,gmSpatialModel-method gmSpatialModel-class logratioVariogram,gmSpatialModel-method variogram,gmSpatialModel-method
MPS training image classgmTrainingImage-class
General description of a spatial modelgmUnconditionalSpatialModel-class
Validation strategy descriptiongmValidationStrategy-class
Superclass for grid or nothingGridOrNothing-class
Compute covariance matrix oout of locationsgsi.calcCgram
Cokriging of all sorts, internal functiongsi.Cokriging
Internal function, conditional turning bands realisationsgsi.CondTurningBands
Workhorse function for direct samplinggsi.DS
Empirical variogram or covariance function in 2Dgsi.EVario2D
Empirical variogram or covariance function in 3Dgsi.EVario3D
Reorganisation of cokriged compositionsgsi.gstatCokriging2compo gsi.gstatCokriging2compo.data.frame gsi.gstatCokriging2compo.default gsi.gstatCokriging2rmult gsi.gstatCokriging2rmult.data.frame gsi.gstatCokriging2rmult.default
extract information about the original data, if availablegsi.getV gsi.orig
Create a matrix of logcontrasts and name prefixgsi.produceV
Internal function, unconditional turning bands realisationsgsi.TurningBands
Check presence of missings check presence of missings in a data.framehas.missings.data.frame
Plot an image of gridded dataimage_cokriged image_cokriged.default image_cokriged.spatialGridAcomp image_cokriged.spatialGridRmult
Plot variogram maps for anisotropic logratio variogramsimage.logratioVariogramAnisotropy
Image method for mask objectsimage.mask
Check for any anisotropy classis.anisotropySpecification
Check for anisotropy of a theoretical variogramis.isotropic
Create a parameter set of local for neighbourhood specification.KrigingNeighbourhood
Specify the leave-one-out strategy for validation of a spatial modelLeaveOneOut
Length, and number of columns or rowslength.gmCgram ncol.gmCgram nrow.gmCgram
Create a anisotropic model for regionalized compositionsLMCAnisCompo
Empirical logratio variogram calculationlogratioVariogram
Variogram method for gmSpatialModel objectslogratioVariogram_gmSpatialModel variogram_gmSpatialModel
Logratio variogram of a compositional datalogratioVariogram,acomp-method
Generalised diagonalisations Calculate several generalized diagonalisations out of a data set and its empirical variogramgenDiag 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
Construct a Gaussian gmSpatialModel for regionalized compositionsmake.gmCompositionalGaussianSpatialModel
Construct a Multi-Point gmSpatialModel for regionalized compositionsmake.gmCompositionalMPSSpatialModel
Construct a Gaussian gmSpatialModel for regionalized multivariate datamake.gmMultivariateGaussianSpatialModel
Mean accuracymean.accuracy
Average measures of spatial decorrelationmean.spatialDecorrelationMeasure
Structural function model specificationModelStructuralFunctionSpecification-class
Number of directions of an empirical variogramndirections ndirections.azimuth ndirections.azimuthInterval ndirections.default ndirections.gmEVario ndirections.gstatVariogram ndirections.logratioVariogram ndirections.logratioVariogramAnisotropy
Specify a strategy for validation of a spatial modelNfoldCrossValidation
National Geochemical Survey of Australia: soil dataNGSAustralia
Test for lack of spatial correlationnoSpatCorr.test noSpatCorr.test.data.frame noSpatCorr.test.default noSpatCorr.test.matrix
Multiple maps Matrix of maps showing different combinations of components of a composition, user definedpairsmap pairsmap.default pairsmap.SpatialPointsDataFrame
Plot method for accuracy curvesplot.accuracy
Draw cuves for covariance/variogram modelsplot.gmCgram
Plot empirical variogramsplot.gmEVario
Plot variogram lines of empirical directional logratio variogramsplot.logratioVariogramAnisotropy
Plotting method for swarmPlot objectsplot.swarmPlot
Precision calculationsprecision precision.accuracy
Predict method for objects of class 'gmSpatialModel'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
Predict method for generalised diagonalisation objectspredict.genDiag
Compute model variogram values Evaluate the variogram model provided at some lag vectorspredict.LMCAnisCompo
Print method for mask objectsprint.mask
Compositional maps, pairwise logratios Matrix of maps showing different combinations of components of a composition, in pairwise logratiospwlrmap
Create a parameter set specifying a gaussian sequential simulation algorithmSequentialSimulation
Generate D-variate variogram modelsgsi.validModels setCgram vg.Exp vg.exp vg.Exponential vg.Gau vg.Gauss vg.gauss vg.Sph vg.sph vg.Spherical
Set or get the ordering of a gridgetGridOrder gridOrder_array gridOrder_GSLib gridOrder_gstat gridOrder_sp setGridOrder setGridOrder_array setGridOrder_sp
Set a mask on an objectsetMask setMask.data.frame setMask.DataFrameStack setMask.default setMask.GridTopology setMask.SpatialGrid setMask.SpatialPoints
Reorder data in a gridsortDataInGrid
Compute diagonalisation measuresspatialDecorrelation spatialDecorrelation.gmEVario spatialDecorrelation.gstatVariogram spatialDecorrelation.logratioVariogram
Construct a regionalized composition / reorder compositional simulationsspatialGridAcomp
Construct a regionalized multivariate dataspatialGridRmult
Spectral colors palette based on the RColorBrewer::brewer.pal(11,"Spectral")spectralcolors
Spherifying transform Compute a transformation that spherifies a certain data setsphTrans sphTrans.default
Get/set name/index of (non)stacking dimensionsnoStackDim noStackDim.default stackDim stackDim.DataFrameStack stackDim<- stackDim<-.default
Get name/index of the stacking dimension of a Spatial objectstackDim,Spatial-method
Plot a swarm of calculated output through a DataFrameStackswarmPlot
Swath plotsswath swath.acomp swath.ccomp swath.default swath.rcomp
Create a parameter set specifying a turning bands simulation algorithmTurningBands
Unmask a masked objectunmask unmask.data.frame unmask.DataFrameStack unmask.SpatialPixels unmask.SpatialPoints
Validate a spatial modelvalidate validate.LeaveOneOut validate.NfoldCrossValidation
Quick plotting of empirical and theoretical variograms Quick and dirty plotting of empirical variograms/covariances with or without their modelsvariogramModelPlot variogramModelPlot.gmEVario
Quick plotting of empirical and theoretical variograms Quick and dirty plotting of empirical variograms/covariances with or without their modelsvariogramModelPlot.gstatVariogram
Quick plotting of empirical and theoretical logratio variograms Quick and dirty plotting of empirical logratio variograms with or without their modelsvariogramModelPlot.logratioVariogram
Ore composition of a bench at a mine in Windarling, West Australia.Windarling
Write a regionalized data set in GSLIB formatwrite.GSLib
Cross-validation errror measuresxvErrorMeasures xvErrorMeasures.data.frame xvErrorMeasures.DataFrameStack
Cross-validation errror measuresxvErrorMeasures.default