kergp: Kernel laboratory. This package, created during the ReDICE consortium, has been enriched with new functionalities:
categorical variables, radial kernels, optimizer choices, etc.
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lineqGPR : Gaussian Process Regression Models with Linear Inequality Constraints.
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nestedKriging : Nested kriging models for large data sets.
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mixgp: Kriging models with both discrete and continuous input variables. Will be included in kergp.
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Gaussian Processes For Computer Experiments,
F. Bachoc, E. Contal, H. Maatouk, and D. Rullière (2017), ESAIM: Proceedings and surveys, proceedings of MAS2016 conference, 60, p. 163-179.
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Gaussian Process Modulated Cox Processes under Linear Inequality Constraints,
A. F. López-Lopera, S. John, and N. Durrande (2019), PMLR:, proceedings of AISTATS19 conference, 89, p. 1997-2006.
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Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC,
A. F. López-Lopera, F. Bachoc, N. Durrande, J. Rohmer, D. Idier, and O. Roustant (2019), to appear in Monte Carlo and Quasi-Monte Carlo Methods:, proceedings of MCQMC18 conference, 13, p. 355-371.
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(*)
One of the Chair activities is to develop opensource R packages
that are later available on the CRAN archive website.
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