funGp: Gaussian Process Models for Scalar and Functional Inputs

Construction and smart selection of Gaussian process models with emphasis on treatment of functional inputs. 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). 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], Deborah Idier [ctb], Jeremy Rohmer [ctb]
MaintainerJose Betancourt <>
Package repositoryView on CRAN
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funGp documentation built on July 22, 2021, 9:07 a.m.