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.
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