See also NEURAL NETWORKS. In this past June's issue of R journal, the 'neuralnet' package was introduced. I had recently been familiar with utilizing neural networks via the 'nnet' package (see my post on Data Mining in A Nutshell) but I find the neuralnet package more useful because it will allow you to actually plot the network nodes and connections. (it may be possible to do this with nnet, but I'm not aware of how).The neuralnet package was written primarily for multilayer perceptron architectures, which may be a limitation if you are interested in other architectures.The data set used was a default data set found in the package 'datasets' and consisted of 248 observations and 8 variables:"education" "age" "parity" "induced" "case" "spontaneous" "stratum" "pooled.stratum"The following code runs the network (with 2 hidden layers) classifying 'case' (a binary variable) as a function of several independent varaibles. The neural network is estimated, and the results are stored in the data frame 'nn.'nn