funGp: Gaussian Process Models for Scalar and Functional Inputs

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 <>.

Package details

AuthorJose Betancourt [cre, aut], Fran├žois Bachoc [aut], Thierry Klein [aut], Jeremy Rohmer [aut], Yves Deville [ctb], Deborah Idier [ctb]
MaintainerJose Betancourt <>
Package repositoryView on CRAN
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funGp documentation built on May 29, 2024, 8 a.m.