Sensitivity Analysis


Methods and functions for global sensitivity analysis.


The sensitivity package implements some global sensitivity analysis methods:

Model managing

The sensitivity package has been designed to work either models written in R than external models such as heavy computational codes. This is achieved with the input argument model present in all functions of this package.

The argument model is expected to be either a funtion or a predictor (i.e. an object with a predict function such as lm).

X is the design of experiments, i.e. a data.frame with p columns (the input factors) and n lines (each, an experiment), and y is the vector of length n of the model responses.

The model in invoked once for the whole design of experiment.

The argument model can be left to NULL. This is refered to as the decoupled approach and used with external computational codes that rarely run on the statistician's computer. See decoupling.


Gilles Pujol, Bertrand Iooss, Alexandre Janon with contributions from Paul Lemaitre for the PLI function, Laurent Gilquin for the sobolroalhs, sobolroauc and sobolSalt functions, Loic le Gratiet for the sobolGP function, Khalid Boumhaout, Taieb Touati and Bernardo Ramos for the sobolowen and soboltouati functions, Jana Fruth for the PoincareConstant, support, sobolTIIlo and sobolTIIpf functions, Sebastien Da veiga for the sensiFdiv and sensiHSIC functions, Joseph Guillaume for the delsa and parameterSets functions, Olivier Roustant for the PoincareOptimal function, Frank Weber, Thibault Delage and Roelof Oomen.

(maintainer: Bertrand Iooss


R. Faivre, B. Iooss, S. Mahevas, D. Makowski, H. Monod, editors, 2013, Analyse de sensibilite et exploration de modeles. Applications aux modeles environnementaux, Editions Quae.

B. Iooss and A. Saltelli, 2017, Introduction: Sensitivity analysis. In: Springer Handbook on Uncertainty Quantification, R. Ghanem, D. Higdon and H. Owhadi (Eds), Springer. href

B. Iooss and P. Lemaitre, 2015, A review on global sensitivity analysis methods. In Uncertainty management in Simulation-Optimization of Complex Systems: Algorithms and Applications, C. Meloni and G. Dellino (eds), Springer.

A. Saltelli, K. Chan and E. M. Scott eds, 2000, Sensitivity Analysis, Wiley.

A. Saltelli, P. Annoni, I. Azzini, F. Campolongo, M. Ratto and S. Tarantola, 2010, Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Computer Physics Communications 181, 259–270.

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