neuralnet: Training of Neural Networks
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
N, 1993) is implemented.
Version:
1.44.2
Depends:
R (≥ 2.9.0)
Imports:
grid,
MASS, grDevices, stats, utils,
Deriv
Published:
2019年02月07日
Author:
Stefan Fritsch [aut],
Frauke Guenther [aut],
Marvin N. Wright [aut, cre],
Marc Suling [ctb],
Sebastian M. Mueller [ctb]
Maintainer:
Marvin N. Wright <wright at leibniz-bips.de>
NeedsCompilation:
no
Documentation:
Downloads:
Reverse dependencies:
Reverse imports:
AriGaMyANNSVR,
CEEMDANML,
ConvertPar,
DeepLearningCausal,
EventDetectR,
FRI,
FWRGB,
Imneuron,
ImNN,
LilRhino,
Modeler,
nnfor,
reddPrec,
RSDA,
SignacX,
trackdem,
traineR,
WaveletML
Reverse suggests:
flowml,
gemR,
innsight,
mcboost,
misspi,
mlr,
NeuralNetTools,
NeuralSens,
plotmo,
qeML,
TrafficBDE
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