kergp: Gaussian Process Laboratory
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: |
0.5.7 |
| 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: |
2024-02-05 |
| DOI: |
10.32614/CRAN.package.kergp |
| Author: |
Yves Deville, David Ginsbourger, Olivier Roustant. Contributors: Nicolas Durrande. |
| Maintainer: |
Olivier Roustant <roustant at insa-toulouse.fr> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| CRAN checks: |
kergp results |
Documentation:
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