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.
|Author||Yves Deville, David Ginsbourger, Olivier Roustant. Contributors: Nicolas Durrande.|
|Maintainer||Olivier Roustant <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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