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: 0.5.0
Depends: Rcpp (≥ 0.10.5), methods, testthat, nloptr, lattice
Imports: MASS, numDeriv, stats4, doParallel, doFuture, utils
LinkingTo: Rcpp
Suggests: DiceKriging, DiceDesign, lhs, inline, foreach, knitr, ggplot2, reshape2, corrplot
Published: 2019-09-30
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

Downloads:

Reference manual: kergp.pdf
Package source: kergp_0.5.0.tar.gz
Windows binaries: r-devel: kergp_0.5.0.zip, r-release: kergp_0.5.0.zip, r-oldrel: kergp_0.5.0.zip
OS X binaries: r-release: kergp_0.5.0.tgz, r-oldrel: kergp_0.5.0.tgz
Old sources: kergp archive

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