Package: RSNNS 0.4-17

RSNNS: Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.

Authors:Christoph Bergmeir [aut, cre, cph], José M. Benítez [ths], Andreas Zell [ctb], Niels Mache [ctb], Günter Mamier [ctb], Michael Vogt [ctb], Sven Döring [ctb], Ralf Hübner [ctb], Kai-Uwe Herrmann [ctb], Tobias Soyez [ctb], Michael Schmalzl [ctb], Tilman Sommer [ctb], Artemis Hatzigeorgiou [ctb], Dietmar Posselt [ctb], Tobias Schreiner [ctb], Bernward Kett [ctb], Martin Reczko [ctb], Martin Riedmiller [ctb], Mark Seemann [ctb], Marcus Ritt [ctb], Jamie DeCoster [ctb], Jochen Biedermann [ctb], Joachim Danz [ctb], Christian Wehrfritz [ctb], Patrick Kursawe [ctb], Andre El-Ama [ctb]