kergp: Gaussian Process Laboratory (original) (raw)
Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation.
| Version: | 0.5.8 |
|---|---|
| Depends: | Rcpp (≥ 0.10.5), methods, testthat, nloptr, lattice |
| Imports: | MASS, numDeriv, stats4, doParallel, doFuture, utils |
| LinkingTo: | Rcpp |
| Suggests: | DiceKriging, DiceDesign, inline, foreach, knitr, ggplot2, reshape2, corrplot |
| Published: | 2024-11-19 |
| DOI: | 10.32614/CRAN.package.kergp |
| Author: | Yves Deville  [aut], David Ginsbourger [aut], Olivier Roustant [aut, cre], Nicolas Durrande [ctb] |
| Maintainer: | Olivier Roustant |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | kergp results |
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