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spup

R package: spup - Spatial Uncertainty Propagation Analysis

Description

Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) and Brown and Heuvelink (2007). 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.

Installation

R package spup is available on CRAN and can be installed in R as:

install.packages("spup")

The development version from GitHub can be install via:

library(devtools)
install_github("ksawicka/spup")

References

Brown, J. D. and G. B. M. Heuvelink (2007). "The Data Uncertainty Engine (DUE): A software tool for assessing and simulating uncertain environmental variables." Computers & Geosciences 33(2): 172-190.

Heuvelink, G. B. M., et al. (2007). "A probabilistic framework for representing and simulating uncertain environmental variables." International Journal of Geographical Information Science 21(5): 497-513.

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Version

Install

install.packages('spup')

Monthly Downloads

177

Version

1.4-0

License

GPL (>= 3)

Maintainer

Kasia Sawicka

Last Published

January 10th, 2024

Functions in spup (1.4-0)

makeCRM

Defining a spatial correlogram model
quantile_MC_sgdf

quantile() function for MC sample saved in a SpatialGridDataFrame
propagate

Propagation function
var_MC_sgdf

var() function for MC sample saved in a SpatialGridDataFrame
mean_MC_sgdf

mean() function for MC sample saved in a SpatialGridDataFrame
spup--pkg

spup - Package for spatial uncertainty propagation
template

Constructor for class "template".
genSample

Methods for generating Monte Carlo realizations from uncertain inputs.
render.character

Render method for "character" class.
render

Rendering template
woon

Neighbourhood in Rotterdam.
print.template

Print method for class "template."
vgm2crm

Convert vgm to crm
varcov

Calculate variance covariance matrix
plot.SpatialCorrelogramModel

Plots correlogram model
list_depth

Function to find the level of list nesting
render.template

Render method for "template" class.
sd_MC_sgdf

sd() function for MC sample saved in a SpatialGridDataFrame
stratsamp

Stratified sampling for spatial variables
defineMUM

Define Mulivariate Uncertainty Model
OC

Soil organic carbon content in a south area (33 x 33km) of lake Alaotra in Madagascar.
dem30m

Digital Elevation Model of Zlatibor region in Serbia.
TN

Soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar.
TN_sd

Standard deviation of soil total nitrogen content in a south area (33 x 33km) of lake Alaotra in Madagascar.
check_distribution

Simple check if distribution provided in defineUM() belongs to a list of supported distributions.
OC_sd

Standard deviation of soil organic carbon content in a south area (33 x 33km) of lake Alaotra in Madagascar.
crm2vgm

Converting a spatial correlogram model to a variogram model
check_if_Spatial

Simple check if class of provided object is Spatial
defineUM

Define an uncertainty model for a single variable
distribution_sampling_raster

Sampling from a given distribution
distribution_sampling

Sampling from a given distribution
genSample.JointScalar

Generating sample from cross-correlated variables described by a scalar.
genSample.MarginalCategoricalSpatial

Generating Monte Carlo sample from an uncertain object of a class 'MarginalCategoricalSpatial'
dem30m_sd

Standard deviation of Digital Elevation Model of Zlatibor region in Serbia.
find_strata

Sampling from a given distribution
executable

Wrapper function for calling executables in R
genSample.MarginalNumericSpatial

Generating Monte Carlo sample from an uncertain object of a class 'MarginalNumericSpatial'
genSample.MarginalScalar

Generating Monte Carlo sample from an uncertain object of a class 'MarginalScalar'
genSample.JointNumericSpatial

Generating Monte Carlo sample from a list of uncertain objects that are cross-correlated.