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.


Reference manual

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0.5.5 by Olivier Roustant, a month ago

Browse source code at

Authors: Yves Deville , David Ginsbourger , Olivier Roustant. Contributors: Nicolas Durrande.

Documentation:   PDF Manual  

GPL-3 license

Imports MASS, numDeriv, stats4, doParallel, doFuture, utils

Depends on Rcpp, methods, testthat, nloptr, lattice

Suggests DiceKriging, DiceDesign, inline, foreach, knitr, ggplot2, reshape2, corrplot

Linking to Rcpp

See at CRAN