Sobol Indices for Models with Fixed and Stochastic Parameters
Autoplot implementations
Bootstrap Sobol indices from stored samples
Estimate Failure Probability from Simulator Outputs
Fast Ishigami Test Function
Simulate one unit in the simple process
Time to M successes for one individual
QoI wrapper for the process model
Process model for a matrix of individuals
Create Sobol Sampling Designs
Example 3: Large covariate dependent random effect
Example 4: Slight covariate dependent random effect
Example 1: Deterministic G-function (reference case)
Example 5: Sobol indices for the process model
Example 2: Random effect on the output (constant Gaussian noise)
Sobol G-function (Saltelli reference function) - C++ backend
Sobol G-function (Saltelli reference function)
Additive Gaussian noise on the Sobol G-function (k = 2)
Additive Gaussian noise on the Sobol G-function (k = 2) - C++ backend
Sobol G-function restricted to the first two inputs - C++ backend
Quantity-of-interest wrapper for the covariate noisy G-function (k = 2...
QoI wrapper for covariate noisy G-function (k = 2) - C++ backend
Quantity-of-interest wrapper for the noisy G-function (k = 2)
QoI wrapper for the noisy G-function (k = 2) - C++ backend
Sobol G-function restricted to the first two inputs
Additive Gaussian noise on the Sobol G-function (k = 2)
Covariate dependent Gaussian noise on the Sobol G-function (k = 2) - C...
Sobol Indices for Stochastic Simulators
Reliability-Oriented Sobol Indices
Two-step clinic model wrapper for Sobol designs
Design generation for Sobol indices
M/M/1 queue model wrapper for Sobol designs
Generic QoI-based Sobol indices for a stochastic model
Run Sobol analysis with optional QoI wrapper
Sobol4R-package
Summarise Sobol Indices
Tools to design experiments, compute Sobol sensitivity indices, and summarise stochastic responses inspired by the strategy described by Zhu and Sudret (2021) <doi:10.1016/j.ress.2021.107815>. Includes helpers to optimise toy models implemented in C++, visualise indices with uncertainty quantification, and derive reliability-oriented sensitivity measures based on failure probabilities. It is further detailed in Logosha, Maumy and Bertrand (2022) <doi:10.1063/5.0246026> and (2023) <doi:10.1063/5.0246024> or in Bertrand, Logosha and Maumy (2024) <https://hal.science/hal-05371803>, <https://hal.science/hal-05371795> and <https://hal.science/hal-05371798>.
Useful links