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

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 Sobolâ€™ 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:

```
A. Puy, S. Lo Piano, A. Saltelli, S. A. Levin (2022). sensobol: Computation of
Variance-Based Sensitivity Indices. Journal of Statistical Software 102(5),
1-37. doi:10.18637/jss.v102.i05.
```

A BibTex entry for LaTex users is:

```
@article{,
author = {Puy, Arnald and {Lo Piano}, Samuele and Saltelli, Andrea and Levin, Simon A.},
journal = {Journal of Statistical Software},
title = {{sensobol: an R package to compute variance-based sensitivity indices}},
doi = {10.18637/jss.v102.i05},
volume = {102},
number = {5},
pages = {1--37},
year = {2022}
}
```