Gaussian Process Laboratory R package kergp version 0.5.8 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. Software 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 Olivier Roustant <roustant@insa-toulouse.fr> Comprehensive R Archive Network (CRAN) Yves Deville [aut] (ORCID: <https://orcid.org/0000-0002-1233-488X>), David Ginsbourger [aut] (ORCID: <https://orcid.org/0000-0003-2724-2678>), Olivier Roustant [aut, cre], Nicolas Durrande [ctb] GPL-3 2024-11-19 application/tgz https://CRAN.R-project.org/package=kergp doi:10.32614/CRAN.package.kergp