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 <https://hal.archives-ouvertes.fr/hal-02532713>.
| Version: |
0.3.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
methods, foreach, knitr, scales, microbenchmark, doFuture, doRNG, future, progressr |
| Published: |
2023-01-22 |
| Author: |
Jose Betancourt [cre, aut],
François Bachoc [aut],
Thierry Klein [aut],
Jeremy Rohmer [aut],
Yves Deville [ctb],
Deborah Idier [ctb] |
| Maintainer: |
Jose Betancourt <djbetancourt at uninorte.edu.co> |
| BugReports: |
https://github.com/djbetancourt-gh/funGp/issues |
| License: |
GPL-3 |
| URL: |
https://djbetancourt-gh.github.io/funGp/,
https://github.com/djbetancourt-gh/funGp |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
funGp results |
Documentation:
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