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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: |
0.5.8 |
| 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 <roustant at insa-toulouse.fr> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| CRAN checks: |
kergp results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=kergp
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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