High Dimensional Model Representation (HDMR) is an efficient technique which decomposes a multivariate function into a constant, univariate, bivariate functions and so on. These functions are forced to be mutually orthogonal by means of an orthogonality condition. The technique which isgenerally used for high-dimensional input-output systems can be applied to various disciplines including sensitivity analysis, differential equations, inversion of data and so on. In this article we present a computer program that computes individual components of HDMR resolution of a given multivariate function. The program also calculates the global sensitivity indices. Lastly the results of the numerical experiments for different set of functions are introduced.

H. KAYA, M. KAPLAN and H. SAYGIN, “A Recursive Algorithm for Finding HDMR Terms for Sensitivity Analysis“, Computer Physics Communications, Vol. 158, No.2, pp. 106–112 (2004).