# sensobol

The goal of `sensobol`

is to provide a set of functions to swiftly compute and visualize up to third-order Sobol’ sensitivity indices. The functions allow to: - Create the sample matrices for the model evaluation. - Compute and bootstrap up to third-order effects. - Assess the approximation error of Sobol’ indices. - Plot the model uncertainty and the Sobol’ indices.

## 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)
## Create sample matrix to compute first, total and second-order indices:
A <- sobol_matrices(n = 1000, k = 3, second = TRUE)
## Compute the model output (using the Ishigami test function):
Y <- ishigami_Mapply(A)
## Compute the Sobol' indices (first, total and second-order):
sens <- sobol_indices(Y = Y, params = colnames(data.frame(A)), R = 100, n = 1000, second = TRUE)
```

## Citation

Please use the following citation if you use `sensobol`

in your publications:

```
Arnald Puy (2019). sensobol: Computation of High-Order Sobol' Sensitivity Indices. R package
version 0.2.0 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 = {2019},
note = {R package version 0.2.0},
url = {http://github.com/arnaldpuy/sensobol},
}
```