### What is GAMLSS

GAMLSS is a modern distribution-based approach to (semiparametric) regression models, where all the parameters of the assumed distribution for the response can be modelled as additive functions of the explanatory variables.

NEWS: A short course on GAMLSS will be held in Verona, Italy on the 23 and 24 of November.

### 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. The book Flexible Regression and Smoothing: Using GAMLSS in R , shows using practical examples, how GAMLSS models can be fitted. For more recent work and examples see the vignettes. For short courses on GAMLSS see Short Courses

### How to learn more about GAMLSS

The book Flexible Regression and Smoothing: Using GAMLSS in R is published by CRC Press. Two others books are in preparation i) Distributions for Location Scale and Shape, the GAMLSS implementation in R iii) Generalised Additive Models for Location Scale and Shape: A Distributional Regression Approach. Draft versions of some of the books and other booklets can be found in Books & Articles The GAMLSS article on the Journal of Statistical Software can be useful (but a bit outdated) for a short introduction. Also some useful material can be found on short course booklets.

### What distributions can be used within GAMLSS

GAMLSS provide over 90 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.

### What additive terms can be used within GAMLSS

P-splines, Cubic splines, loess smoothing, ridge regression, lasso regression, 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 random effect models from the lme() function of the nlme package, smoothers from the gam() function (of package mgcv), the neural network function nnet() (of package nnet) , and decision threes (of package apart).

### 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 centile estimation, marine research, medicine, meteorology, rainfalls, vaccines, e.t.c. The World Health Organisation, the International Monetary Fund and the European Commission are some of international organisations who used GAMLSS.

## Inflated and zero adjusted Distributions