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bluefoxr
COINr:Composite Indicator Construction and Analysis
A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.
Maintained by William Becker. Last updated 2 months ago.
26 stars 8.94 score 73 scripts 1 dependents
mbinois
GPareto:Gaussian Processes for Pareto Front Estimation and Optimization
Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.
Maintained by Mickael Binois. Last updated 1 years ago.
16 stars 5.96 score 38 scripts 1 dependents
arnaldpuy
sensobol:Computation of Variance-Based Sensitivity Indices
It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) <doi:10.18637/jss.v102.i05>.
Maintained by Arnald Puy. Last updated 1 years ago.
15 stars 5.57 score 50 scripts