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Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some predefined kernels are available, such as radial or tensorsum 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 leaveoneout validation.
Package details 


Author  Yves Deville, David Ginsbourger, Olivier Roustant. Contributors: Nicolas Durrande. 
Maintainer  Olivier Roustant <roustant@insatoulouse.fr> 
License  GPL3 
Version  0.5.5 
Package repository  View on CRAN 
Installation 
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