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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0.5.1 by Olivier Roustant, a year 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, lhs, inline, foreach, knitr, ggplot2, reshape2, corrplot

Linking to Rcpp

See at CRAN