Ultimately, SAFE aims at contributing towards improving the diffusion and quality of GSA practice in the environmental modelling community. SAFE is open source and freely available for academic and non-commercial purpose. MATLAB offers a couple of LHS functions in the Statistics and Machine Learning Toolbox: 'lhsdesign' (Latin hypercube sample) and 'lhsnorm' (Latin hypercube sampling from normal distribution). The documentation includes a set of workflow scripts with practical guidelines on how to apply GSA and how to use the toolbox. The toolbox is designed to make GSA accessible to non-specialist users, and to provide a fully commented code for more experienced users to complement their own tools. Furthermore, SAFE includes numerous visualisation tools for the effective investigation and communication of GSA results. All methods implemented in SAFE support the assessment of the robustness and convergence of sensitivity indices. X lhsnorm (mu,sigma,n) returns an n -by- p matrix, X, containing a Latin hypercube sample of size n from a p -dimensional multivariate normal distribution with mean vector, mu, and covariance matrix, sigma. It implements several established GSA methods and allows for easily integrating others. Here we present a Matlab/Octave toolbox for the application of GSA, called SAFE (Sensitivity Analysis For Everybody). Global Sensitivity Analysis (GSA) is increasingly used in the development and assessment of environmental models.
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