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). 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 <>.

Version: 0.3.0
Depends: R (≥ 3.5.0)
Imports: methods, foreach, knitr, scales, microbenchmark, doFuture, doRNG, future, progressr
Published: 2022-05-30
Author: Jose Betancourt [cre, aut], Fran├žois Bachoc [aut], Thierry Klein [aut], Jeremy Rohmer [aut], Yves Deville [ctb], Deborah Idier [ctb]
Maintainer: Jose Betancourt <djbetancourt at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: funGp results


Reference manual: funGp.pdf


Package source: funGp_0.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): funGp_0.3.0.tgz, r-oldrel (arm64): funGp_0.3.0.tgz, r-release (x86_64): funGp_0.3.0.tgz, r-oldrel (x86_64): funGp_0.3.0.tgz
Old sources: funGp archive


Please use the canonical form to link to this page.