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Along with increased complexity of the models used for scientific activities and engineeringcome diverse and greater uncertainties. Today, effectively quantifying the uncertainties containedin a model appears to be more important than ever. Scientific fellows know how serious it is tocalibrate their model in a robust way, and decision-makers describe how critical it is to keep the besteffort to reduce the uncertainties about the model. Effectively accessing the uncertainties about themodel requires mastering all the tasks involved in the numerical experiments, from optimizing theexperimental design to managing the very time consuming aspect of model simulation and choosingthe adequate indicators and analysis methods.In this paper, we present an open framework for organizing the complexity associated withnumerical model simulation and analyses. Named mtk (Mexico Toolkit), the developed system aimsat providing practitioners from different disciplines with a systematic and easy way to compare andto find the best method to effectively uncover and quantify the uncertainties contained in the modeland further to evaluate their impact on the performance of the model. Such requirements imply thatthe system must be generic, universal, homogeneous, and extensible. This paper discusses such animplementation using the R scientific computing platform and demonstrates its functionalities withexamples from agricultural modeling.The package mtk is of general purpose and easy to extend. Numerous methods are alreadyavailable in the actual release version, including Fast, Sobol, Morris, Basic Monte-Carlo, Regression,LHS (Latin Hypercube Sampling), PLMM (Polynomial Linear metamodel). Most of them are compiledfrom available R packages with extension tools delivered by package mtk.

mtk: A General-Purpose and Extensible R Environment for Uncertainty and Sensitivity Analyses of Numerical Experiments