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, 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.
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