Ridge Group Sparse Optimization Problem for Estimation of a Meta Model Based on Reproducing Kernel Hilbert Spaces

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Documentation for package ‘RKHSMetaMod’ version 1.1

• DESCRIPTION file.

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RKHSMetaMod-package	Set of Rcpp and R functions to produce a sequence of meta models that are the solutions of the RKHS Ridge Group Sparse or RKHS Group Lasso optimization problems, calulate their associated prediction errors as well as their empirical sensitivity indices.
calc_Kv	Function to calculate the Gram matrices and their eigenvalues and eigenvectors for a chosen reproducing kernel.
grplasso_q	Function to fit a solution with q active groups of an RKHS Group Lasso problem.
mu_max	Function to find the maximal value of the penalty parameter in the RKHS Group Lasso problem.
pen_MetMod	Function to fit a solution of the RKHS Ridge Group Sparse problem.
PredErr	Function to calculate the prediction error.
RKHSgrplasso	Function to fit a solution of an RKHS Group Lasso problem.
RKHSMetaMod	Set of Rcpp and R functions to produce a sequence of meta models that are the solutions of the RKHS Ridge Group Sparse or RKHS Group Lasso optimization problems, calulate their associated prediction errors as well as their empirical sensitivity indices.
RKHSMetMod	Function to produce a sequence of meta models that are the solutions of the RKHS Ridge Group Sparse or RKHS Group Lasso optimization problems.
RKHSMetMod_qmax	Function to produce a sequence of meta models, with at most qmax active groups in each meta model. The meta models are the solutions of the RKHS Ridge Group Sparse or RKHS Group Lasso optimization problems.
SI_emp	Function to calculate the empirical sensitivity indices for an input or a group of inputs.