SALib: An open-source Python library for Sensitivity Analysis
By Jon Herman, Will Usher. 2017
1,329 citations recorded by the source corpus.
SALib contains Python implementations of commonly used global sensitivity analysis methods, including Sobol (Sobol’ 2001, Andrea Saltelli (2002), Andrea Saltelli et al. (2010)), Morris (Morris 1991 ...
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Citations in the corpus
- InterpretML: A Unified Framework for Machine Learning Interpretability (2019, background): “(Pedregosa et al., 2011), plotly (Plotly, 2015), lime (Ribeiro et al., 2016), shap (Lundberg and Lee, 2017), SALib (Herman and Usher, 2017), partial dependence (Friedman, 2001), Jupyter (Kluyver et al., 2016), pandas (McKinney), and more.”
- CASSL: Curriculum Accelerated Self-Supervised Learning (2017, methodology): “Instead, we use Sobol sensitivity analysis [29] implementation in SAlib and propose a data-driven method for estimating the sensitivity metrics.”
- The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “These operationalize the various SA methods via different programming languages, and include Dakota (Adams et al., 2020) in C ++ , SobolGSA (Kucherenko and Zaccheus, 2020) in C#, MATLAB, and Python, UQLab (Marelli and Sudret, 2014) in MATLAB, OpenTURNS (Baudin et al., 2017) in Python and C ++ , the ”