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RKHSMetaMod (version 1.1)

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

Description

Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) ).

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Version

Install

install.packages('RKHSMetaMod')

Monthly Downloads

49

Version

1.1

License

GPL (>= 2)

Maintainer

Halaleh Kamari

Last Published

July 6th, 2019

Functions in RKHSMetaMod (1.1)

Function to calculate the Gram matrices and their eigenvalues and eigenvectors for a chosen reproducing kernel.

Function to find the maximal value of the penalty parameter in the RKHS Group Lasso problem.

Function to calculate the prediction error.

Function to produce a sequence of meta models that are the solutions of the RKHS Ridge Group Sparse or RKHS Group Lasso optimization problems.

Function to calculate the empirical sensitivity indices for an input or a group of inputs.

Function to fit a solution of an RKHS Group Lasso problem.

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.

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.

Function to fit a solution of the RKHS Ridge Group Sparse problem.

Function to fit a solution with q active groups of an RKHS Group Lasso problem.