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"))

## Arguments

… |
For function `n()` a formula of the type `~x1+x2+x3` that specifies
the covariates that should be modeled by the neural network. For function `predictn()` ,
arguments to be passed to `predict.bamlss` . |

k |
For function `n()` , the number of hidden nodes of the network. Note that one can set
an argument `split = TRUE` to split up the neural network into, e.g., `nsplit = 5`
parts with `k` nodes each. For function `n.weights()` , argument `k`
is the number of input variables of the network (number of covariates). |

type |
Integer. Type `1` fits a complete network in each boosting iteration, `type = 2` selects
the best fitting node in each boosting iteration. for function `n.weights()` , the type of
activation function that should be used. For function `predictn()` , the type of prediction
that should be computed. |

nodes |
Number of nodes for each layer, i.e., can also be a vector. |

r, s |
Parameters controlling the shape of the activation functions. |

x |
A scaled covariate matrix, the data will be used to identify the range of the weights. |

object, data |
See `smooth.construct` . For function `predictn()` ,
a boosted `"bamlss"` object. |

dropout |
The fraction of inner weights that should be set to zero. |

newdata |
The data frame that should be used for prediction. |

model |
For which parameter of the distribution predictions should be computed. |

mstop |
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. |

## Value

Function `n()`

, similar to function `s`

a simple smooth specification
object.

## See also

## Examples

## ... coming soon ...!