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: | 0.1.0 |
| Imports: | methods, foreach, knitr, scales, qdapRegex, microbenchmark, doFuture, future, progressr |
| Published: | 2020-04-22 |
| Author: | Jose Betancourt [cre, aut], François 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 |
| Reference manual: | funGp.pdf |
| Package source: | funGp_0.1.0.tar.gz |
| Windows binaries: | r-devel: funGp_0.1.0.zip, r-release: funGp_0.1.0.zip, r-oldrel: funGp_0.1.0.zip |
| macOS binaries: | r-release: funGp_0.1.0.tgz, r-oldrel: funGp_0.1.0.tgz |
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