{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"SALib implements global sensitivity analysis methods (Sobol, Morris, FAST, DGSM, PAWN, HDMR, and others) to quantify how model inputs affect outputs in systems modeling and uncertainty analysis.","skillfed_tags":["sensitivity-analysis","uncertainty-quantification","systems-modeling"],"use_cases":["Identify which model parameters drive uncertainty in climate or environmental simulations.","Screen a large parameter space to find the most influential inputs before detailed calibration.","Quantify first-order and total-order effects of inputs on model outputs for publication or reporting.","Compare sensitivity across different model variants or scenarios using standardized indices.","Reduce computational cost by focusing model refinement on parameters with high sensitivity."],"what_it_does":"SALib is a Python library for global sensitivity analysis\u2014a set of statistical methods to determine which inputs to a computational model have the largest effect on its outputs. It implements established algorithms including Sobol indices (for variance-based decomposition), Morris screening (for one-at-a-time parameter sweeps), FAST and eFAST (Fourier-based methods), DGSM (derivative-based measures), PAWN, HDMR, and fractional factorial designs. The library is designed for systems modelers, researchers, and engineers who need to understand parameter importance, identify influential factors, or reduce model complexity.\n\nYou provide a problem specification (parameter names, bounds, and output names), use one of SALib's sampling methods to generate parameter combinations, evaluate your model on those samples, then apply an analysis method to extract sensitivity indices with confidence intervals. SALib supports both a procedural API and a newer method-chaining interface via ProblemSpec. It depends on numpy, scipy, matplotlib, pandas, and multiprocess for numerical computation and visualization.","worth_installing":"Yes. SALib is a mature, actively maintained library with no known vulnerabilities, low install friction, and permissive licensing. It is the standard Python tool for global sensitivity analysis and is suitable for academic research, engineering, and systems modeling work. Install it if you need to understand parameter importance or uncertainty propagation in a computational model."},"id":"salib","links":{"html":"https://skillfed.io/packages/salib","md":"https://skillfed.io/packages/salib.md","pypi":"https://pypi.org/project/salib/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-10-12","license_spdx":"MIT","license_treatment":"permissive","name":"SALib","python_support":"supports_current","summary":"Tools for global sensitivity analysis. Contains Sobol', Morris, FAST, DGSM, PAWN, HDMR, Moment Independent and fractional factorial methods"},"popularity":{"monthly_downloads":737913,"position":5187,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.2"}