  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).
