# 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 third-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 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)
```

## 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},
}
```