Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. Smart selection is based on Ant Colony Optimization ACO algorithm.
This package was first developed in the frame of the RISCOPE research project, funded by the French Agence Nationale de la Recherche (ANR) for the period 2017-2021 (ANR, project No. 16CE04-0011, RISCOPE.fr), and certified by SAFE Cluster.
Main methods
fgpm: creation of funGp regression models
predict,fgpm-method: output estimation at new input points based on a funGp model
simulate,fgpm-method: random sampling from a funGp Gaussian process model
update,fgpm-method: modification of data and hyperparameters of a funGp model
Plotters
plot,fgpm-method: validation plot for a fgpm model
plot.predict.fgpm: plot of predictions based on a fgpm model
plot.simulate.fgpm: plot of simulations based on a fgpm model
Main method
fgpm_factory: structural parameter optimization
Functions for pre-optimization
decay: regularized initial pheromones
decay2probs: normalized initial pheromones
Plotters post-optimization
plot,Xfgpm-method: plot of the evolution of the algorithm with which = "evolution"
or of the absolute and relative quality of the optimized model with which = "diag"
Correction post-optimization of input data structures
which_on: indices of active inputs in a model
structure delivered by fgpm_factory
get_active_in: extraction of active input
data based on a model structure delivered by fgpm_factory
Manual: funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs (doi:10.18637/jss.v109.i05 )
Paper: - Gaussian process metamodeling of functional-input code for coastal flood hazard assessment (doi:10.1016/j.ress.2020.106870 )
Tech. report: Ant Colony Based Model Selection for Functional-Input Gaussian Process Regression (https://hal.science/hal-02532713)
Déborah Idier and Jérémy Rohmer