This is support for the smoother functions ga() and ba() intefaces for Simon Woood's gam() and bam() functions from package mgcv.
It is not intended to be called directly by users.
gamlss.ga(x, y, w, xeval = NULL, ...)
gamlss.ba(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.
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/).
Wood S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.