{ "_id": "6a1055f9acfb0bcc41ca27fd", "Package": "spup", "Type": "Package", "Title": "Spatial Uncertainty Propagation Analysis", "Version": "1.4-0", "Date": "2024-01-09", "Authors@R": "c(person(\"Kasia\", \"Sawicka\", email = \"katwic55@ceh.ac.uk\", role = c(\"aut\", \"cre\")),\nperson(\"Gerard\", \"Heuvelink\", email = \"gerard.heuvelink@wur.nl\", role = c(\"aut\")),\nperson(\"Dennis\", \"Walvoort\", email = \"dennis.walvoort@wur.nl\", role = c(\"aut\")),\nperson(\"Stefan\", \"van Dam\", email = \"stefan.vandam@wur.nl\", role = c(\"ctb\")),\nperson(\"Damiano\", \"Luzzi\", email = \"damiano.luzzi@wur.nl\", role = c(\"ctb\"))\n)", "Description": "Uncertainty propagation analysis in spatial environmental\nmodelling following methodology described in Heuvelink et al.\n(2007) and Brown and Heuvelink\n(2007) . The package provides\nfunctions for examining the uncertainty propagation starting\nfrom input data and model parameters, via the environmental\nmodel onto model outputs. The functions include uncertainty\nmodel specification, stochastic simulation and propagation of\nuncertainty using Monte Carlo (MC) techniques. Uncertain\nvariables are described by probability distributions. Both\nnumerical and categorical data types are handled. Spatial\nauto-correlation within an attribute and cross-correlation\nbetween attributes is accommodated for. The MC realizations may\nbe used as input to the environmental models called from R, or\nexternally.", "License": "GPL (>= 3)", "LazyData": "true", "VignetteBuilder": "knitr", "URL": "https://github.com/ksawicka/spup", "BugReports": "https://github.com/ksawicka/spup/issues", "RoxygenNote": "7.2.3", "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nlibssl-dev libproj-dev libsqlite3-dev libudunits2-dev", "Repository": "https://ksawicka.r-universe.dev", "Date/Publication": "2024-01-11 12:21:12 UTC", "RemoteUrl": "https://github.com/ksawicka/spup", "RemoteRef": "HEAD", "RemoteSha": "f6434a87a200ff8c0ed139b653055820f914af87", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-09 07:54:05 UTC", "User": "root" }, "Author": "Kasia Sawicka [aut, cre],\nGerard Heuvelink [aut],\nDennis Walvoort [aut],\nStefan van Dam [ctb],\nDamiano Luzzi [ctb]", "Maintainer": "Kasia Sawicka ", "MD5sum": "3711b39b22fe79540a60dc6923835682", "_user": "ksawicka", "_type": "src", 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Alaotra in Madagascar.", "object": "OC_sd", "class": [ "RasterLayer" ], "fields": [], "table": false, "tojson": false }, { "name": "TN", "title": "Soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar.", "object": "TN", "class": [ "RasterLayer" ], "fields": [], "table": false, "tojson": false }, { "name": "TN_sd", "title": "Standard deviation of soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar.", "object": "TN_sd", "class": [ "RasterLayer" ], "fields": [], "table": false, "tojson": false }, { "name": "woon", "title": "Neighbourhood in Rotterdam.", "object": "woon", "class": [ "SpatialPolygonsDataFrame" ], "fields": [], "table": false, "tojson": false } ], "_help": [ { "page": "check_distribution", "title": "Simple check if distribution provided in defineUM() belongs to a list of supported distributions.", "topics": [ "check_distribution" ] }, { "page": "check_if_Spatial", "title": "Simple check if class of provided object is Spatial", "topics": [ "check_if_Spatial" ] }, { "page": "crm2vgm", "title": "Converting a spatial correlogram model to a variogram model", "topics": [ "crm2vgm" ] }, { "page": "defineMUM", "title": "Define Mulivariate Uncertainty Model", "topics": [ "defineMUM" ] }, { "page": "defineUM", "title": "Define an uncertainty model for a single variable", "topics": [ "defineUM" ] }, { "page": "dem30m", "title": "Digital Elevation Model of Zlatibor region in Serbia.", "topics": [ "dem30m" ] }, { "page": "dem30m_sd", "title": "Standard deviation of Digital Elevation Model of Zlatibor region in Serbia.", "topics": [ "dem30m_sd" ] }, { "page": "distribution_sampling", "title": "Sampling from a given distribution", "topics": [ "distribution_sampling" ] }, { "page": "distribution_sampling_raster", "title": "Sampling from a given distribution", "topics": [ "distribution_sampling_raster" ] }, { "page": "executable", "title": "Wrapper function for calling executables in R", "topics": [ "executable" ] }, { "page": "find_strata", "title": "Sampling from a given distribution", "topics": [ "find_strata" ] }, { "page": "genSample", "title": "Methods for generating Monte Carlo realizations from uncertain inputs.", "topics": [ "genSample" ] }, { "page": "genSample.JointNumericSpatial", "title": "Generating Monte Carlo sample from a list of uncertain objects that are cross-correlated.", "topics": [ "genSample.JointNumericSpatial" ] }, { "page": "genSample.JointScalar", "title": "Generating sample from cross-correlated variables described by a scalar.", "topics": [ "genSample.JointScalar" ] }, { "page": "genSample.MarginalCategoricalSpatial", "title": "Generating Monte Carlo sample from an uncertain object of a class 'MarginalCategoricalSpatial'", "topics": [ "genSample.MarginalCategoricalSpatial" ] }, { "page": "genSample.MarginalNumericSpatial", "title": "Generating Monte Carlo sample from an uncertain object of a class 'MarginalNumericSpatial'", "topics": [ 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