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The Team

M. Stasinopoulos Prof. Mikis Stasinopoulos: co-creator of GAMLSS, coauthor of `
Flexible Regression and Smoothing: Using  GAMLSS  in R’ developing and maintaining the software , STORM School of Computing, London Metropolitan University, UK and partner in GS analytics LLP.

Dr. Bob Rigby co-creator of GAMLSS, coauthor of `Flexible Regression and Smoothing: Using  GAMLSS  in R’  and developing of distributions and contributing in theory, (STORM, School of Computing, London Metropolitan University, UK).

Prof. Gillian Heller  coauthor of `Flexible Regression and Smoothing:  Using  GAMLSS  in R’. and development and applications in actuarial statistics, (Head of Department, Macquarie University Australia).

Dr. Vlasios Voudouris  coauthor of `Flexible Regression and Smoothing:  Using  GAMLSS  in R’. and developing forecasting in GAMLSS, (Argus Media, London ).
Dr. Fernanda De Bastiani  coauthor of `Flexible Regression and Smoothing:  Using  GAMLSS  in R’. and developing of GAMLSS spatial (Federal University of Pernambuco, Brazil).

Prof. Paul Eilers giving valuable advice on smoothing and statistical modelling in general (Erasmus University, Holland).

Dr Nikolaos Georgikopoulos development, applications and consultant in finance and banking (risk management)m (Stern School of Business-New York University).

Dr Marco Enea development and applications of the zero inflated and zero and one adjusted models and discrete distributions (University of Palermo, Italy).

Prof. Andreas Mayr, boosting and GAMLSS (Department of Statistics, LMU Munich).

Dr. Luiz Nakamura, development and application of continuous distributions (Federal University of Santa Catarina, Brazil).


The original GAMLSS implementation was done in GLIM by Mikis Stasinopoulos and Bob Rigby. The translation from GLIM to R was done in the early 2002 by Mikis Stasinopoulos, Bob Rigby and Popi Akanziliotou. For the more current versions the following people have voluntarily contributed to the GAMLSS software:

  • Elaine Borghie contributed in the improvement of functions centiles.pred()centiles.split() and Q.stats()
  • Paul Eilers contributed in the creation of the functions scattersmooth(), pb(), cy() and pvc() and in the package gamlss.demo
  • Steve Ellison contributed to the centiles() function
  • Michael Hohle corrected the function gamlssNP()
  • Larisa Kosidou contributed in the improvement of the package gamlss.demo
  • Brian Marx contributed to the package gamlss.demo
  • Nicoleta Mortan contributed into the creation of the package gamlss.cens
  • Raydonal Ospina contributed the BEOI and BEZI distributions for package gamlss.dist
  • Konstantinos Pateras contributed by creating the nice interfaces in the package gamlss.demo

The following people whose function(s) have been adapted for the GAMLSS software:

  • Gareth Amber for his fractional polynomial function which the gamlss fp() function is based.
  • John Chambers and Trevor Hastie for their R function step.gam() on which the gamlss function stepGAIC.CH() is based
  • Jochen Einbeck, Ross Darnell and John Hinde for their functions alldist() and allvc() from the package npmlreg on which the function gamlssNP() is based
  • Trevor Hastie for the function random()
  • Jim Lindsey and Philippe Lambert for their function stableglm() in the package stable on which the function nlgamlss() is based
  • Brian Ripley for his function nnet() to which the gamlss function nn() provides an interface and for the function multinom() which is used in our function gamlssMX()
  • Stefan van Buuren for his original worm plot function on which the gamlss wp() function is based.
  • Venables and Ripley for their R function stepAIC() on which the gamlss function stepGAIC.VR() is based.
  • Simon Wood for his function gam() to which the gamlss function ga() provides an interface.


We also like to thank the following people who have contributed by suggesting changes or by reporting bugs:

  • Christian Kiffner for suggesting changes for the function term.plot()
  • Albert Wong for suggesting changes to the function lpred() applied to interval response variables.
  • Tim Cole for several suggestions for improvement of the gamlss software.
  • Huiqi Pan for several suggestions to improve centile estimation in gamlss.
  • Willem Vervoort for reporting the problems for calling gamlss() within other functions.