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

TL;DR

The 'sensobol' R package offers comprehensive, user-friendly tools for variance-based sensitivity analysis, including estimation of various indices and visualization, applicable to scalar or multivariate models.

Contribution

It introduces a versatile R package that implements state-of-the-art sensitivity estimators, enabling efficient analysis of models with multiple outputs and higher-order effects.

Findings

Successfully analyzed classic models demonstrating package capabilities

Provides accurate estimation of first, total, and third-order sensitivity indices

Facilitates visualization of sensitivity analysis results

Abstract

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. Its flexibility makes it also appropriate for models with either a scalar or a multivariate output. We illustrate its functionality by conducting a variance-based sensitivity analysis of three classic models: the Sobol' (1998) G function, the logistic population growth model of Verhulst (1845), and the spruce budworm and forest model of Ludwig, Jones and Holling (1976).