Those functions are support for the functions pb(), pbo(), ps(), ridge(), ri(), cy(), pvc(), and pbm().
The functions are not intended to be called directly by users.
gamlss.pb(x, y, w, xeval = NULL, ...)
gamlss.pbo(x, y, w, xeval = NULL, ...)
gamlss.ps(x, y, w, xeval = NULL, ...)
gamlss.ri(x, y, w, xeval = NULL, ...)
gamlss.cy(x, y, w, xeval = NULL, ...)
gamlss.pvc(x, y, w, xeval = NULL, ...)
gamlss.pbm(x, y, w, xeval = NULL, ...)
gamlss.pbz(x, y, w, xeval = NULL, ...)
gamlss.pbc(x, y, w, xeval = NULL, ...)
gamlss.pbp(x, y, w, xeval = NULL, ...)the x for function gamlss.fp is referred to the design matric of the specific parameter model (not to be used by the user)
the y for function gamlss.fp is referred to the working variable of the specific parameter model (not to be used by the user)
the w for function gamlss.fp is referred to the iterative weight variable of the specific parameter model (not to be used by the user)
used in prediction
further arguments passed to or from other methods.
All function return fitted smoothers.
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.
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.org/).