Resources

Links and software of interest

Software

Software packages and addons that are useful for performing sensitivity analysis.

💻 OpenTURNS, an open source C++/Python library for the treatment of uncertainties, risks and statistics. See also its GUI: Persalys.

💻 UQLab, a MATLAB-based uncertainty quantification framework with state-of-the art, highly optimized open source algorithms (and good documentation).

💻 SIML@B provides a set of online tools to perform uncertainty analysis and sensitivity analysis (UASA) of model output.

💻 sensobol, An R Package to Compute Variance-Based Sensitivity Indices.

💻 SALib, Sensitivity Analysis Library in Python. Includes Sobol’, FAST, DGSM, PAWN, moment independent.

Books

The following references give a good guide to sensitivity analysis and related topics.

📖 Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D. Saisana, M., Tarantola, S., 2008, Global Sensitivity Analysis. The Primer, John Wiley & Sons publishers.

📖 S. Da Veiga, F. Gamboa, B. Iooss and C. Prieur. Basics and trends in sensitivity analysis - Theory and practice in R, SIAM, 2021.

📖 Interpretability for Industry 4.0: Statistical and Machine Learning Approaches, A. Lepore, B. Palumbo and J-M. Poggi (Eds), Springer, 2022

📖 Borgonovo, E. (2017). Sensitivity Analysis An Introduction for the Management Scientist, (E. Borgnovo, Ed.). Springer.

📖 Cacuci, D. G. (2003). Sensitivity and uncertainty analysis. Volume I, Theory. Chapman & Hall/CRC Press.

📖 Cacuci, D. G., Ionescu-Bujor, M., & Navon, M. (2003). Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems. CRC Press.

📖 de Rocquigny, E., Devictor, Nicolas., & Tarantola, Stefano. (2008). Uncertainty in industrial practice: A guide to quantitative uncertainty management. John Wiley & Sons.

📖 Kozlova, M., & Yeomans, J. S. (Eds.). (2024). Sensitivity Analysis for Business, Technology, and Policymaking (1st edition). Routledge.

📖 Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice., John Wiley & Sons, Ltd.

Links

🔗 [GdR MASCOT-NUM]((https://uq.math.cnrs.fr/) is a French Research Group dealing with stochastic methods for the analysis of numerical codes. Its main objective is to coordinate research efforts in this scientific area, which is often called design, modeling and analysis of computer experiments.