Package: intamap 1.5-7

intamap: Procedures for Automated Interpolation

Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

Authors:Edzer Pebesma [aut], Jon Olav Skoien [aut, cre], Olivier Baume [ctb], A Chorti [ctb], Dionisis Hristopulos [ctb], Hannes Kazianka [ctb], Stepahnie Melles [ctb], Giannis Spiliopoulos [ctb]

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intamap/json (API)

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

Peer review:

Datasets:

On CRAN:

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

2.60 score 1 stars 2 packages 67 scripts 629 downloads 24 exports 60 dependencies

Last updated 1 years agofrom:4669f0079e. Checks:OK: 7. Indexed: yes.

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Doc / VignettesOKOct 24 2024
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Exports:bayesCopulablockPredictcheckSetupcoarsenGridconformProjectionscopulaEstimationcreateIntamapObjectestimateAnisotropyestimateParametersgenerateTimeModelsgetIntamapParamsgetInterpolationMethodNamesinterpolateinterpolateBlockmethodParametersplotIntamappostProcesspredictTimepreProcessrotateAnisotropicDataspatialPredictsummaryIntamapunbiasedKrigeyamamotoKrige

Dependencies:abindautomapBHclassclassIntclicodetoolscolorspaceDBIdoParallele1071evdfansifarverFNNforeachggplot2gluegstatgtableintervalsisobanditeratorsKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixMBAmgcvmunsellmvtnormnlmepillarpkgconfigplyrproxyR6RColorBrewerRcppreshaperlangs2scalessfsftimespspacetimestarstibbleunitsutf8vctrsviridisLitewithrwkxtszoo

Readme and manuals

Help Manual

Help pageTopics
A package providing methods for automatic interpolation: pre-processing, parameter estimation, spatial prediction and post processingintamap-package
Performs spatial interpolation using copulasbayesCopula
Spatial block predictionblockPredict spatialPredict.block
check setupcheckSetup
Coarsening of a spatial gridcoarsenGrid
Getting conformed projectionsconformProjections
ML-estimation of the spatial copula model parameterscopulaEstimation
Create an object for interpolation within the intamap packagecreateIntamapObject
estimateAnisotropyestimateAnisotropy
Automatic estimation of correlation structure parametersestimateParameters estimateParameters.automap estimateParameters.copula estimateParameters.default estimateParameters.idw estimateParameters.linearVariogram estimateParameters.transGaussian estimateParameters.yamamoto
estimateTimeModelestimateTimeModel
Generate time modelsgenerateTimeModels
Setting parameters for the intamap packagegetIntamapParams
get interpolation method namesgetInterpolationMethodNames
Example data for the intamap packageobservations
Simulated Intamap ObjectintamapExampleObject
spatial interpolationinterpolate interpolateBlock
generate string for generation of method parametersmethodParameters methodParameters.copula methodParameters.default methodParameters.idw
plot intamap objectsplot.automap plot.copula plot.default plot.idw plot.linearVariogram plot.transGaussian plot.yamamoto plotIntamap
pre-process datapostProcess postProcess.default postProcess.idw
Time prediction for intamap package methodspredictTime
pre-processing of datapreProcess preProcess.default preProcess.idw
rotateAnisotropicDatarotateAnisotropicData
Spatial predictionspatialPredict spatialPredict.automap spatialPredict.copula spatialPredict.default spatialPredict.idw spatialPredict.linearVariogram spatialPredict.transGaussian spatialPredict.yamamoto
summary intamap objectssummary.automap summary.copula summary.idw summary.linearVariogram summary.transGaussian summary.yamamoto summaryIntamap
models for estimating prediction time in intamap packagetimeModels
Unbiased krigingunbiasedKrige
kriging and simulation with alternative kriging varianceyamamotoKrige