News: The beta version 0.3.0 is now available at Github. It contains new implementations based on the additive constrained GP model in (López-Lopera et al., 2022). Numerical examples are given in the folder "notebooks", and in the folder "demo" from lineqGPR_0.3.0.tar.
Updates: The beta version 0.2.0 is now available at Github. It contains new implementations based on the MaxMod algorithm proposed in (Bachoc et al., 2020). Numerical examples are given in the folder "notebooks", and in the folder "demo" from lineqGPR_0.2.0.tar.
Description: lineqGPR is a package for Gaussian process interpolation, regression and simulation under linear inequality constraints based on (López-Lopera et al., 2017). The constrained models are given as objects with "lineqGP" S3 class. Implementations according to (Maatouk and Bay, 2017) are also provided as objects with "lineqDGP" S3 class.
Note: lineqGPR was developed within the frame of the Chair in Applied Mathematics OQUAIDO, gathering partners in technological research (BRGM, CEA, IFPEN, IRSN, Safran, Storengy) and academia (CNRS, Ecole Centrale de Lyon, Mines Saint-Etienne, University of Grenoble, University of Nice, University of Toulouse) around advanced methods for Computer Experiments.
Authors: Andrés Felipe López-Lopera (Mines Saint-Étienne) with contributions from Olivier Roustant (INSA Toulouse).
Maintainer: Andrés Felipe López-Lopera, anfelopera@utp.edu.co
References
A. F. López-Lopera, F. Bachoc, N. Durrande and O. Roustant (2018), "Finite-dimensional Gaussian approximation with linear inequality constraints". SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1224–1255. [doi] [preprint]
F. Bachoc, A. Lagnoux and A. F. López-Lopera (2019), "Maximum likelihood estimation for Gaussian processes under inequality constraints". Electronic Journal of Statistics, 13 (2): 2921-2969. [doi] [preprint]
F. Bachoc, A. F. López-Lopera and O. Roustant (2022), "Sequential construction and dimension reduction of Gaussian processes under inequality constraints". Journal on Mathematics of Data Science [doi] [preprint]
A. F. López-Lopera, F. Bachoc and O. Roustant (2022), "High-dimensional additive Gaussian processes under monotonicity constraints". ArXiv e-prints [preprint]
Maatouk, H. and Bay, X. (2017), "Gaussian process emulators for computer experiments with inequality constraints". Mathematical Geosciences, 49(5): 557-582. [doi]
Roustant, O., Ginsbourger, D., and Deville, Y. (2012), "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization". Journal of Statistical Software, 51(1): 1-55. [doi]