Artificial neural networks are applied in many situations. neuralnet is built to train multilayer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables. Thus, neural networks are used as extensions of generalized linear models. neuralnet is a very flexible package. The backpropagation algorithm and three versions of resilient backpropagation are implemented and it provides a custom-choice of activation and error function. An arbitrary number of covariates and response variables as well as of hidden layers can theoretically be included. The paper gives a brief introduction to multi-layer perceptrons and resilient backpropagation and demonstrates the application of neuralnet using the data set infert, which is contained in the R distribution.
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For attribution, please cite this work as
Günther & Fritsch, "The R Journal: neuralnet: Training of Neural Networks", The R Journal, 2010
BibTeX citation
@article{RJ-2010-006,
author = {Günther, Frauke and Fritsch, Stefan},
title = {The R Journal: neuralnet: Training of Neural Networks},
journal = {The R Journal},
year = {2010},
note = {https://doi.org/10.32614/RJ-2010-006},
doi = {10.32614/RJ-2010-006},
volume = {2},
issue = {1},
issn = {2073-4859},
pages = {30-38}
}