This smooth constructor implements single hidden layer neural networks.
## The neural network smooth constructor. n(..., k = 10, type = 2) ## Initialize weights. n.weights(nodes, k, r = NULL, s = NULL, type = c("sigmoid", "gauss", "softplus", "cos", "sin"), x = NULL, ...) ## Second weights initializer, internally calls n.weights. make_weights(object, data, dropout = 0.2) ## Boosted neural net predictions. predictn(object, newdata, model = NULL, mstop = NULL, type = c("link", "parameter"))
Number of nodes for each layer, i.e., can also be a vector.
Parameters controlling the shape of the activation functions.
A scaled covariate matrix, the data will be used to identify the range of the weights.
The fraction of inner weights that should be set to zero.
The data frame that should be used for prediction.
For which parameter of the distribution predictions should be computed.
The stopping iteration for which predictions should be computed. The default is to return a matrix of predictions, each column represents the prediction of one boosting iteration.
n(), similar to function
s a simple smooth specification
## ... coming soon ...!