sensitivity

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 21 articles )

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  1. Champagne, Clara; Gerhards, Maximilian; Lana, Justin; García Espinosa, Bernardo; Bradley, Christina; González, Oscar; Cohen, Justin M.; Le Menach, Arnaud; White, Michael T.; Pothin, Emilie: Using observed incidence to calibrate the transmission level of a mathematical model for \textitPlasmodiumvivax dynamics including case management and importation (2022)
  2. Wang, Yan; Lu, Guichen; Du, Jiang: Calibration and prediction for the inexact SIR model (2022)
  3. Arnald Puy, Samuele Lo Piano, Andrea Saltelli, Simon A. Levin: sensobol: an R package to compute variance-based sensitivity indices (2021) arXiv
  4. Broto, Baptiste; Bachoc, François; Depecker, Marine; Martinez, Jean-Marc: Gaussian linear approximation for the estimation of the Shapley effects (2021)
  5. Hintz, Erik; Hofert, Marius; Lemieux, Christiane: Normal variance mixtures: distribution, density and parameter estimation (2021)
  6. Martin, Olivier; Fernandez-Diclo, Yasmil; Coville, Jérôme; Soubeyrand, Samuel: Equilibrium and sensitivity analysis of a spatio-temporal host-vector epidemic model (2021)
  7. Lamboni, Matieyendou: Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices (2020)
  8. Qian, George; Mahdi, Adam: Sensitivity analysis methods in the biomedical sciences (2020)
  9. Roustant, Olivier; Gamboa, Fabrice; Iooss, Bertrand: Parseval inequalities and lower bounds for variance-based sensitivity indices (2020)
  10. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)
  11. Gu, Mengyang: Jointly robust prior for Gaussian stochastic process in emulation, calibration and variable selection (2019)
  12. Mercadier, Cécile; Roustant, Olivier: The tail dependograph (2019)
  13. Chadsuthi, Sudarat; Wichapeng, Surapa: The modelling of hand, foot, and mouth disease in contaminated environments in Bangkok, Thailand (2018)
  14. Grenier, Emmanuel; Helbert, Celine; Louvet, Violaine; Samson, Adeline; Vigneaux, Paul: Population parametrization of costly black box models using iterations between SAEM algorithm and Kriging (2018)
  15. Lamboni, Matieyendou: Global sensitivity analysis: a generalized, unbiased and optimal estimator of total-effect variance (2018)
  16. Hosseini, Bamdad; Stockie, John M.: Estimating airborne particulate emissions using a finite-volume forward solver coupled with a Bayesian inversion approach (2017)
  17. Scholten, Lisa; Schuwirth, Nele; Reichert, Peter; Lienert, Judit: Tackling uncertainty in multi-criteria decision analysis -- an application to water supply infrastructure planning (2015) ioport
  18. Rohmer, Jeremy: Combining meta-modeling and categorical indicators for global sensitivity analysis of long-running flow simulators with spatially dependent inputs (2014)
  19. Pau, George Shu Heng; Zhang, Yingqi; Finsterle, Stefan: Reduced order models for many-query subsurface flow applications (2013)
  20. 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

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