Variance-based global sensitivity analysis of numerical models using R

TL;DR

This paper explores variance-based global sensitivity analysis of complex models using R, comparing two packages, sensobol and sensitivity, with illustrative examples to guide users.

Contribution

It provides a comparative study of two R packages for global sensitivity analysis, highlighting their features and usability for complex black-box models.

Findings

Sensitivity package handles dependent inputs well

Sensobol offers user-friendly visualization tools

Illustrative examples facilitate learning both packages

Abstract

Sensitivity analysis plays an important role in the development of computer models/simulators through identifying the contribution of each (uncertain) input factor to the model output variability. This report investigates different aspects of the variance-based global sensitivity analysis in the context of complex black-box computer codes. The analysis is mainly conducted using two R packages, namely sensobol (Puy et al., 2021) and sensitivity (Iooss et al., 2021). While the package sensitivity is equipped with a rich set of methods to conduct sensitivity analysis, especially in the case of models with dependent inputs, the package sensobol offers a bunch of user-friendly tools for the visualisation purposes. Several illustrative examples are supplied that allow the user to learn both packages easily and benefit from their features.