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)

## Define the base sample size and the parameters
N <- 1000
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_Mapply(mat)

## Compute and bootstrap the Sobol' indices:
sens <- sobol_indices(Y = Y, N = N, params = params, boot = TRUE, R = 100)

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 0.2.2 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},
  }