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>.

Version: 0.2.1
Imports: methods, foreach, knitr, scales, qdapRegex, microbenchmark, doFuture, future, progressr
Published: 2020-11-24
Author: Jose Betancourt [cre, aut], Fran├žois Bachoc [aut], Thierry Klein [aut], Deborah Idier [ctb], Jeremy Rohmer [ctb]
Maintainer: Jose Betancourt <djbetancourt at uninorte.edu.co>
License: GPL-3
URL: https://djbetancourt-gh.github.io/funGp/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: funGp results

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Reference manual: funGp.pdf
Package source: funGp_0.2.1.tar.gz
Windows binaries: r-devel: funGp_0.2.0.zip, r-release: funGp_0.2.0.zip, r-oldrel: funGp_0.2.0.zip
macOS binaries: r-release: funGp_0.2.0.tgz, r-oldrel: funGp_0.2.0.tgz
Old sources: funGp archive

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