  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 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.
