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>
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:, r-release:, r-oldrel:
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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