Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Christoph Bergmeir, José M. Benítez
Title: Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS
Abstract: Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a) encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b) accessibility of all of the SNNS algorithmic functionality from R using a low-level interface, and (c) a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNS file formats.

Page views:: 33884. Submitted: 2010-12-20. Published: 2012-01-30.
Paper: Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS     Download PDF (Downloads: 49837)
RSNNS_0.4-3.tar.gz: R source package Download (Downloads: 943; 927KB)
v46i07.R: R example code from the paper Download (Downloads: 1201; 10KB)

DOI: 10.18637/jss.v046.i07

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.