Title: Functions to Generate fMRI Data Including Activated Data, Noise
Description: The package allows users to generate fMRI time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments.

Authors@R: c(person("Marijke", "Welvaert", role = c("aut"),
person("Joke", "Durnez", role = c("ctb")),
person("Beatrijs", "Moerkerke", role = c("ctb")),
person("Yves", "Rosseel", role = c("ctb")),
person("Karsten", "Tabelow", role = c("ctb", "cre"),
person("Geert", "Verdoolaege", role = c("ctb")))
Description: Generates functional Magnetic Resonance Imaging (fMRI)
functions are created for fast data generation with only
a few arguments and a diversity of functions to define
activation and noise. For more advanced users it is possible
to use the low-level functions and manipulate the arguments.

Description: RBF and MLP neural networks with graphical user interface
Packaged: Wed Jun  8 18:47:33 2005; hornik

Packaged: Wed Sep 14 13:21:33 2005; hornik

Packaged: Thu Mar  9 11:52:27 2006; hornik

Author: Stefan Fritsch <fritsch@bips.uni-bremen.de>,  Frauke Guenther <guenther@bips.uni-bremen.de>, following earlier work by Marc Suling <suling@bips.uni-bremen.de>
Description: Training of neural networks using the Resilient Backpropagation with (Riedmiller, 1994) or without Weightbacktracking (Riedmiller, 1993) or the modified globally convergent version by Anastasiadis et. al. (2005). The package allows flexible settings through custom-choice of error- and activation-function. Furthermore the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.
Packaged: Tue Apr 15 14:30:48 2008; fritsch

Packaged: Wed May  7 15:15:16 2008; fritsch

Packaged: Wed Oct  8 15:31:40 2008; fritsch

Author: Stefan Fritsch <fritsch@bips.uni-bremen.de>, Frauke Guenther
<guenther@bips.uni-bremen.de>, following earlier work by Marc
Description: Training of neural networks using backpropagation,
resilient backpropagation with (Riedmiller, 1994) or without
weight backtracking (Riedmiller and Braun, 1993) or the
modified globally convergent version by Anastasiadis et al.
(2005). The package allows flexible settings through
custom-choice of error and activation function. Furthermore,
the calculation of generalized weights (Intrator O & Intrator