sensobol: an R package to compute variance-based sensitivity indices

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.

Installation

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)

Example

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)

Citation

Please use the following citation if you use sensobol in your publications:

A. Puy, S. Lo Piano, A. Saltelli, S. A. Levin (2021). sensobol: Computation of
  Variance-Based Sensitivity Indices. arxiv:2101.10103.

A BibTex entry for LaTex users is:

@Manual{,
    title = {{sensobol}: {C}omputation of Variance-Based Sensitivity Indices},
    author = {Arnald Puy and Samuele Lo Piano and Andrea Satelli and Simon A. Levin},
    journal = {arxiv:2101.10103},
    year = {2021},
    url = {https://github.com/arnaldpuy/sensobol},
  }