GMDHreg: Regression using GMDH Algorithms

Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko. Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, and GMDH MIA (Multilayered Iterative Algorithm) using PRESS (Predicted Residual Error Sum of Squares Statistic) criteria. It is calculated as the sums of squares of the prediction residuals for those observations. An introduction of GMDH algorithms: Farlow, S.J. (1981): "The GMDH algorithm of Ivakhnenko", The American Statistician, 35(4), pp. 210-215. <doi:10.2307/2683292> Ivakhnenko A.G. (1968): "The Group Method of Data Handling - A Rival of the Method of Stochastic Approximation", Soviet Automatic Control, 13(3), pp. 43-55.

Version: 0.1.0
Depends: R (≥ 2.15)
Imports: stats, utils
Published: 2019-03-07
Author: Manuel Villacorta Tilve
Maintainer: Manuel Villacorta Tilve <mvt.oviedo at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: GMDHreg results

Downloads:

Reference manual: GMDHreg.pdf
Package source: GMDHreg_0.1.0.tar.gz
Windows binaries: r-devel: GMDHreg_0.1.0.zip, r-release: GMDHreg_0.1.0.zip, r-oldrel: GMDHreg_0.1.0.zip
OS X binaries: r-release: GMDHreg_0.1.0.tgz, r-oldrel: GMDHreg_0.1.0.tgz

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