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 <https://hal.archives-ouvertes.fr/hal-02532713>.

Package details

AuthorJose Betancourt [cre, aut], Fran├žois Bachoc [aut], Thierry Klein [aut], Deborah Idier [ctb], Jeremy Rohmer [ctb]
MaintainerJose Betancourt <djbetancourt@uninorte.edu.co>
LicenseGPL-3
Version0.2.2
URL https://djbetancourt-gh.github.io/funGp/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("funGp")

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funGp documentation built on July 22, 2021, 9:07 a.m.