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Artificial neural networks are applied in many situations. neuralnet is built to train multi-layer perceptrons in the context of regres- sion analyses, i.e. to approximate functional rela- tionships between covariates and response vari- ables. Thus, neural networks are used as exten- sions of generalized linear models. neuralnet is a very flexible package. The back- propagation algorithm and three versions of re- silient backpropagation are implemented and it provides a custom-choice of activation and er- ror function. An arbitrary number of covariates and response variables as well as of hidden lay- ers 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.

neuralnet: Training of Neural Networks