The R Journal: accepted article

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Spatial Uncertainty Propagation Analysis with the spup R Package PDF download
Kasia Sawicka, Gerard B.M. Heuvelink and Dennis J.J. Walvoort

Abstract Many environmental and geographical models, such as those used in land degradation, agro-ecological and climate studies, make use of spatially distributed inputs that are known im perfectly. The R package spup provides functions for examining the uncertainty propagation from input data and model parameters onto model outputs, via the environmental model. 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 a variable and cross-correlation between variables is accommodated for. The MC realizations may be used as input to the environmental models written in or called from R. This article provides theoretical background and three worked examples that guide users through the application of spup.

Received: 2017-10-03; online 2018-12-07, supplementary material, (3.3 Kb)
CRAN packages: propagate, errors, metRology, spup, gstat, mvtnorm, graphics, magrittr, methods, purrr, raster, whisker, dplyr, GGally, gridExtra, knitr, readr, sp, roxygen2
CRAN Task Views implied by cited CRAN packages: Spatial, SpatioTemporal, ChemPhys, WebTechnologies, Databases, Distributions, Finance, ModelDeployment, Multivariate, ReproducibleResearch

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Kasia Sawicka and Gerard B.M. Heuvelink and Dennis J.J.
  title = {{Spatial Uncertainty Propagation Analysis with the spup R
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-047},
  url = {}