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
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.
GAMLSS provide over 70 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.
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).
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.
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.
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 […]