Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. This package offers: (i) flexible modeling of functionalinput regression problems through the fairly general Gaussian process model; (ii) builtin dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). An indepth tutorial in the use of funGp is provided in Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and Metamodeling background is provided in Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>. The algorithm for structural parameter optimization is described in <https://hal.science/hal02532713>.
Package details 


Author  Jose Betancourt [cre, aut], François Bachoc [aut], Thierry Klein [aut], Jeremy Rohmer [aut], Yves Deville [ctb], Deborah Idier [ctb] 
Maintainer  Jose Betancourt <fungp.rpack@gmail.com> 
License  GPL3 
Version  1.0.0 
URL  https://djbetancourtgh.github.io/funGp/ https://github.com/djbetancourtgh/funGp 
Package repository  View on CRAN 
Installation 
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