Gaussian Process Models for Scalar and Functional Inputs

Construction and smart selection of Gaussian process models with emphasis on treatment of functional inputs. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). Metamodeling background is provided in Betancourt et al. (2020) . The algorithm for structural parameter optimization is described in <>.


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0.2.2 by Jose Betancourt, 5 months ago

Browse source code at

Authors: Jose Betancourt [cre, aut] , Fran├žois Bachoc [aut] , Thierry Klein [aut] , Deborah Idier [ctb] , Jeremy Rohmer [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports methods, foreach, knitr, scales, qdapRegex, microbenchmark, doFuture, future, progressr

See at CRAN