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Generalized Additive Models for Location, Scale and Shape

Statistical modelling at its best

  • Distibutions
  • OriginalGAMLSS
  • centiles
About GAMLSS
01

What is GAMLSS

GAMLSS are (semi) parametric univariate regression models, where all the parameters of the assumed distribution for the response can be modelled as additive functions of the explanatory variables

02

How to use GAMLSS

The GAMLSS framework of statistical modelling is implemented in a series of packages in R. The packages can be downloaded from the R library, CRAN.

03

What distributions can be used within GAMLSS

GAMLSS provide over 80 continuous, discrete and mixed distributions for modelling the response variable. Truncated, censored, log and logit transformed and finite mixture versions of these distributions can be also used.

04

What additive terms can be used within GAMLSS

P-splines, Cubic splines, loess smoothing, ridge regression, simple random effects and varying coefficient models are some of the additive functions provided in the implementation. Appropriate interface is also provided so GAMLSS models can be used in combination with smoothers from the gam() function (of package mgcv) or the neural network function nnet() (of package nnet).

05

Who's is using GAMLSS

GAMLSS has been used in a variety of fields including: actuarial science, biology, biosciences, energy economic, genomics, finance, fisheries, food consumption, growth curves estimation, marine research, medicine, meteorology, rainfalls, vaccines, e.t.c.

06

How to learn more about GAMLSS

Two books on GAMLSS are in preparation: i) The Distribution Toolbox of GAMLSS and ii) GAMLSS Flexible Regression in R. Draft versions of the two books will available in the web soon. Meanwhile, the second edition of original manual provides information on how to use the R-package (dated), the GAMLSS article on the Journal of Statistical Software can be useful for a short introduction and finally the short course booklets.

Blogs and Latest News
  • Version 4.2-7 i) gamlss gamlssML(): now allows the fitting binomial data (sorry it never checked before) and the use of formula in the specification of the model (e.g, y~1) to be consistent with gamlss(). Note that explanatory variables will be ignored if used with gamlssML().  .gamlss.multin.list is now on NAMESPACE  the functions vcov.gamlss() and summary.gamlss() […]

  • This version is released on the 22–6-2013 and it is the first time that robust (sandwich) standard errors are introduce  in gamlss models.  Of course those standard errors apply to parametric GAMLSS models only. When non-parametric smoothing terms are used then  the (sandwich) standard errors can still be used with caution since they are not yet take […]

  •   Version 4.2-5 The most important change in this version of gamlss is the way that the standard errors are calculated. In  previous version the vcov() function was calculated using a final iteration to a non-linear maximisation procedure. This procedure failed in a lot of occasions and the result was that the reported standard errors […]

  • The new version of gamlss is 4.2-0. The following are the changes made:   package gamlss: The functions prof.dev() and prof.term() are improved. The argument step is not anymore compulsory and if not set the argument length is used instead. For most cases there is no need to have a fine grid since the function is approximated using splinefun(). The output is […]

  • The new features in version 2.1-2 are as follows: package gamlss: The function histSmo() is added for density estimation. The function histDist() now has the function gamlssML() as its main fitting function. The fitting function  gamlss() is only used if gamlssML() fails. The function gamlssML() has now an argument start.from. In the function fitDist(), the normal distribution NO() is added to the list of “.realline” so it also appears […]

What did they say
  • All models are wrong but some are useful

    George Box

  • no matter how beautiful your theory, no matter how clever you are or what your name is, if it disagrees with experiment, it’s wrong

    Richard Feynman

  • entities should not be multiplied beyond necessity

    Occam’s Razor

  • Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.

    John W. Tukey