The brnn package was developed by Paulino Perez Rodriguez and Daniel Gianola, and it implements the two-layer Bayesian regularized neural network described in the previous section. The main function in the package is brnn( ) that can be called using the following command:

>brnn(x,y,neurons,normalize,epochs,…,Monte_Carlo,…)

Here, x is an n x p matrix where n is the number of data points and p is the number of variables; y is an n dimensional vector containing target values. The number of neurons in the hidden layer of the network can be specified by the variable neurons. If the indicator function normalize is TRUE, it will normalize the input and output, which is the default option. The maximum number of iterations during model training is specified using epochs. If the indicator binary variable Monte_Carlo is true, then an MCMC method is used to estimate the trace of the inverse of the Hessian matrix A.

Let us try an example with the Auto MPG dataset that we used in Chapter...