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The R
package sensobol
provides several
functions to conduct variance-based uncertainty and sensitivity
analysis, from the estimation of sensitivity indices to the visual
representation of the results. It implements several state-of-the-art
first and total-order estimators and allows the computation of up to
fourth-order effects, as well as of the approximation error, in a swift
and user-friendly way.
To install the stable version on CRAN, use
install.packages("sensobol")
To install the development version, use devtools:
install.packages("devtools") # if you have not installed devtools package already
::install_github("arnaldpuy/sensobol", build_vignettes = TRUE) devtools
This brief example shows how to compute Sobol’ indices. For a more detailed explanation of the package functions, check the vignette.
## Load the package:
library(sensobol)
## Define the base sample size and the parameters
<- 2 ^ 8
N <- paste("X", 1:3, sep = "")
params
## Create sample matrix to compute first and total-order indices:
<- sobol_matrices(N = N, params = params)
mat
## Compute the model output (using the Ishigami test function):
<- ishigami_Fun(mat)
Y
## Compute and bootstrap the Sobol' indices:
<- sobol_indices(Y = Y, N = N, params = params) ind
Please use the following citation if you use sensobol
in
your publications:
Levin (2022). sensobol: Computation of
A. Puy, S. Lo Piano, A. Saltelli, S. A. -Based Sensitivity Indices. Journal of Statistical Software 102(5),
Variance1-37. doi:10.18637/jss.v102.i05.
A BibTex entry for LaTex users is:
@article{,
= {Puy, Arnald and {Lo Piano}, Samuele and Saltelli, Andrea and Levin, Simon A.},
author = {Journal of Statistical Software},
journal = {{sensobol: an R package to compute variance-based sensitivity indices}},
title = {10.18637/jss.v102.i05},
doi = {102},
volume = {5},
number = {1--37},
pages = {2022}
year }
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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