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, ...)
the explanatory variables
iterative y variable
iterative weights
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, Vol. 23, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/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 http://www.gamlss.com/).
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
fk