Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally.
|Depends:||R (≥ 3.3.0)|
|Imports:||graphics, gstat, magrittr, methods, mvtnorm, purrr, raster, sp, whisker|
|Suggests:||dplyr, GGally, gridExtra, knitr, png, readr, testthat|
|Author:||Kasia Sawicka [aut, cre], Gerard Heuvelink [aut], Dennis Walvoort [aut], Stefan van Dam [ctb], Damiano Luzzi [ctb]|
|Maintainer:||Kasia Sawicka <katwic55 at ceh.ac.uk>|
|License:||GPL (≥ 3)|
|CRAN checks:||spup results|
Case study with cross-correlated variables
Case study with spatially variable standard deviation - slope calculations
Case study with calling external model
Case study with categorical data - calculating tax depending on a building function
|Windows binaries:||r-devel: spup_1.3-2.zip, r-release: spup_1.3-2.zip, r-oldrel: spup_1.3-2.zip|
|macOS binaries:||r-release (arm64): spup_1.3-2.tgz, r-oldrel (arm64): spup_1.3-2.tgz, r-release (x86_64): spup_1.3-2.tgz, r-oldrel (x86_64): spup_1.3-2.tgz|
|Old sources:||spup archive|
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