gamlss-package: Generalised Additive Models for Location Scale and Shape

Description Details Author(s) References See Also Examples


Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.


The DESCRIPTION file: This package was not yet installed at build time.
Index: This package was not yet installed at build time.


Mikis Stasinopoulos [aut, cre, cph], Bob Rigby [aut], Vlasios Voudouris [ctb], Calliope Akantziliotou [ctb], Marco Enea [ctb], Daniil Kiose [ctb]

Maintainer: Mikis Stasinopoulos <>


Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models. J. R. Statist. Soc. A., 135 370-384.

Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. Chapman and Hall, London.

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. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in

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,

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

See Also



mod<-gamlss(y~pb(x),,family=BCT, data=abdom, method=mixed(1,20))

Example output

Loading required package: splines
Loading required package:
Loading required package: gamlss.dist
Loading required package: MASS
Loading required package: nlme
Loading required package: parallel
 **********   GAMLSS Version 5.0-2  ********** 
For more on GAMLSS look at
Type gamlssNews() to see new features/changes/bug fixes.

GAMLSS-RS iteration 1: Global Deviance = 4771.925 
GAMLSS-CG iteration 1: Global Deviance = 4771.013 
GAMLSS-CG iteration 2: Global Deviance = 4770.994 
GAMLSS-CG iteration 3: Global Deviance = 4770.994 
	      Summary of the Quantile Residuals
                           mean   =  0.001237747 
                       variance   =  1.001867 
               coef. of skewness  =  -0.004513695 
               coef. of kurtosis  =  2.994576 
Filliben correlation coefficient  =  0.999324 

gamlss documentation built on March 31, 2021, 5:10 p.m.