The R Journal: article published in 2018, volume 10:2

Spatial Uncertainty Propagation Analysis with the spup R Package
Kasia Sawicka, Gerard B.M. Heuvelink and Dennis J.J. Walvoort , The R Journal (2018) 10:2, pages 180-199.

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 imperfectly. 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. The package also accommodates spatial auto-correlation within a variable and cross-correlation between variables. 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 KiB)
CRAN packages: propagate, errors, metRology, spup, gstat, stats, mvtnorm, whisker, shiny
CRAN Task Views implied by cited CRAN packages: ChemPhys, WebTechnologies, Distributions, Finance, Multivariate, Spatial, SpatioTemporal

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

@article{RJ-2018-047,
  author = {Kasia Sawicka and Gerard B.M. Heuvelink and Dennis J.J.
          Walvoort},
  title = {{Spatial Uncertainty Propagation Analysis with the spup R
          Package}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-047},
  url = {https://doi.org/10.32614/RJ-2018-047},
  pages = {180--199},
  volume = {10},
  number = {2}
}