In this paper, we propose an R package, called RKHSMetaMod, that implements a procedure for estimating a meta-model of a complex model. The meta-model approximates the Hoeffding decomposition of the complex model and allows us to perform sensitivity analysis on it. It belongs to a reproducing kernel Hilbert space that is constructed as a direct sum of Hilbert spaces. The estimator of the meta-model is the solution of a penalized empirical least-squares minimization with the sum of the Hilbert norm and the empirical \(L^2\)-norm. This procedure, called RKHS ridge group sparse, allows both to select and estimate the terms in the Hoeffding decomposition, and therefore, to select and estimate the Sobol indices that are non-zero. The RKHSMetaMod package provides an interface from the R statistical computing environment to the C++ libraries Eigen and GSL. In order to speed up the execution time and optimize the storage memory, except for a function that is written in R, all of the functions of this package are written using the efficient C++ libraries through RcppEigen and RcppGSL packages. These functions are then interfaced in the R environment in order to propose a user-friendly package.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-003.zip
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For attribution, please cite this work as
Kamari, et al., "The R Journal: RKHSMetaMod: An R Package to Estimate the Hoeffding Decomposition of a Complex Model by Solving RKHS Ridge Group Sparse Optimization Problem", The R Journal, 2022
BibTeX citation
@article{RJ-2022-003,
author = {Kamari, Halaleh and Huet, Sylvie and Taupin, Marie-Luce},
title = {The R Journal: RKHSMetaMod: An R Package to Estimate the Hoeffding Decomposition of a Complex Model by Solving RKHS Ridge Group Sparse Optimization Problem},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-003},
doi = {10.32614/RJ-2022-003},
volume = {14},
issue = {1},
issn = {2073-4859},
pages = {101-122}
}