funGp

Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). An in-depth tutorial in the use of funGp is provided in Betancourt et al. (2024) doi:10.18637/jss.v109.i05 and Metamodeling background is provided in Betancourt et al. (2020) doi:10.1016/j.ress.2020.106870. The algorithm for structural parameter optimization is described in https://hal.science/hal-02532713.

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