funGp: Gaussian Process Models for Scalar and Functional Inputs
Construction and smart selection of Gaussian process modelsfor analysis of computer experimentswith emphasis on treatment of functional inputs that are regularly sampled. This packageoffers: (i) flexible modeling of functional-input regressionproblems 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).An in-depth tutorial in the use of funGp is provided inBetancourt et al. (2024) <doi:10.18637/jss.v109.i05> andMetamodeling background is provided inBetancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.The algorithm for structural parameter optimization is describedin <https://hal.science/hal-02532713>.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | methods,foreach,knitr,scales,microbenchmark,doFuture,doRNG,future,progressr |
| Published: | 2024-05-10 |
| DOI: | 10.32614/CRAN.package.funGp |
| Author: | Jose Betancourt [cre, aut], François Bachoc [aut], Thierry Klein [aut], Jeremy Rohmer [aut], Yves Deville [ctb], Deborah Idier [ctb] |
| Maintainer: | Jose Betancourt <fungp.rpack at gmail.com> |
| 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 |
| Citation: | funGp citation info |
| Materials: | README,NEWS |
| CRAN checks: | funGp results |
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