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 third-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
devtools::install_github("arnaldpuy/sensobol", build_vignettes = TRUE)
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
N <- 2 ^ 8
params <- paste("X", 1:3, sep = "")
## Create sample matrix to compute first and total-order indices:
mat <- sobol_matrices(N = N, params = params)
## Compute the model output (using the Ishigami test function):
Y <- ishigami_Fun(mat)
## Compute and bootstrap the Sobol' indices:
ind <- sobol_indices(Y = Y, N = N, params = params)
Please use the following citation if you use sensobol
in your publications:
Arnald Puy (2020). sensobol: Computation of High-Order Sobol' Sensitivity Indices. R package
version 0.3.0 http://github.com/arnaldpuy/sensobol
A BibTex entry for LaTex users is:
@Manual{,
title = {sensobol: Computation of High-Order Sobol' Sensitivity Indices},
author = {Arnald Puy},
year = {2020},
note = {R package version 0.3.0},
url = {http://github.com/arnaldpuy/sensobol},
}