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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 |
Reference manual: | kergp.pdf |
Package source: | kergp_0.5.7.tar.gz |
Windows binaries: | r-devel: kergp_0.5.7.zip, r-release: kergp_0.5.7.zip, r-oldrel: kergp_0.5.7.zip |
macOS binaries: | r-release (arm64): kergp_0.5.7.tgz, r-oldrel (arm64): kergp_0.5.7.tgz, r-release (x86_64): kergp_0.5.7.tgz, r-oldrel (x86_64): kergp_0.5.7.tgz |
Old sources: | kergp archive |
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These binaries (installable software) and packages are in development.
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