kergp (0.5.0)

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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.

Maintainer: Olivier Roustant
Author(s): Yves Deville, David Ginsbourger, Olivier Roustant. Contributors: Nicolas Durrande.

License: GPL-3

Uses: doFuture, doParallel, lattice, MASS, nloptr, numDeriv, Rcpp, testthat, ggplot2, inline, lhs, foreach, DiceDesign, DiceKriging, corrplot, reshape2, knitr

Released 4 months ago.

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