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 fourth-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 bud-worm and forest model of Ludwig, Jones, and Holling (1976).
Original language | English (US) |
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Journal | Journal of Statistical Software |
Volume | 102 |
Issue number | 5 |
DOIs | |
State | Published - 2022 |
All Science Journal Classification (ASJC) codes
- Software
- Statistics and Probability
- Statistics, Probability and Uncertainty
Keywords
- R
- modeling
- sensitivity analysis
- uncertainty