R package sensitivity: Global Sensitivity Analysis of Model Outputs. A collection of functions for factor screening, global sensitivity analysis and reliability sensitivity analysis. Most of the functions have to be applied on model with scalar output, but several functions support multi-dimensional outputs.
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References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Arnald Puy, Samuele Lo Piano, Andrea Saltelli, Simon A. Levin: sensobol: an R package to compute variance-based sensitivity indices (2021) arXiv
- Martin, Olivier; Fernandez-Diclo, Yasmil; Coville, Jérôme; Soubeyrand, Samuel: Equilibrium and sensitivity analysis of a spatio-temporal host-vector epidemic model (2021)
- Lamboni, Matieyendou: Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices (2020)
- Qian, George; Mahdi, Adam: Sensitivity analysis methods in the biomedical sciences (2020)
- Roustant, Olivier; Gamboa, Fabrice; Iooss, Bertrand: Parseval inequalities and lower bounds for variance-based sensitivity indices (2020)
- Gu, Mengyang: Jointly robust prior for Gaussian stochastic process in emulation, calibration and variable selection (2019)
- Mercadier, Cécile; Roustant, Olivier: The tail dependograph (2019)
- Lamboni, Matieyendou: Global sensitivity analysis: a generalized, unbiased and optimal estimator of total-effect variance (2018)
- Hosseini, Bamdad; Stockie, John M.: Estimating airborne particulate emissions using a finite-volume forward solver coupled with a Bayesian inversion approach (2017)
- Scholten, Lisa; Schuwirth, Nele; Reichert, Peter; Lienert, Judit: Tackling uncertainty in multi-criteria decision analysis -- an application to water supply infrastructure planning (2015) ioport
- Olivier Roustant; David Ginsbourger; Yves Deville: DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012) not zbMATH