This is support for the smoother function nn() an interface for Brian Reply's `nnet()`

function.
It is not intended to be called directly by users.

`gamlss.nn(x, y, w, xeval = NULL, ...)`

- x
the explanatory variables

- y
iterative y variable

- w
iterative weights

- xeval
if xeval=TRUE then predicion is used

- ...
for extra arguments

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk, Bob Rigby

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
*Appl. Statist.*, **54**, part 3, pp 507-554.

Rigby R.A., Stasinopoulos D. M., Heller G., and De Bastiani F., (2019) *Distributions for Modeling Location, Scale and Shape: Using GAMLSS in R*, Chapman and Hall/CRC.

Ripley, B. D. (1996) *Pattern Recognition and Neural Networks*. Cambridge.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
*Journal of Statistical Software*, **23**(7), 1--46, tools:::Rd_expr_doi("10.18637/jss.v023.i07")

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) *Flexible Regression and Smoothing: Using GAMLSS in R*, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

Venables, W. N. and Ripley, B. D. (2002) *Modern Applied Statistics with S*. Fourth edition. Springer.

`fk`