### What is GAMLSS

GAMLSS are univariate distributional regression models, where all the parameters of the assumed distribution for the response can be modelled as additive functions of the explanatory variables.

### 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. There is a fair amount of documentation on GAMLSS. See the book `Flexible Regression and Smoothing: Using GAMLSS in R', published on April 2017, for a good introduction.

### What distributions can be used within GAMLSS

GAMLSS provide over 100 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. The draft version of the book `Distributions for Modelling Location, Scale and Shape: Using GAMLSS in R' is a good source of information about the implemented distributions.

### What additive terms can be used within GAMLSS

P-splines, Cubic splines, loess smoothing, ridge regression, lasso 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), the neural network function nnet() (of package nnet) and decision threes (of package rpart).

### 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. The World Health Organisation (WHO), the International Monetary Fund (IMF), the European Bank and the Bank of England are among the organisations who use GAMLSS in their analysis.

### How to learn more about GAMLSS

One book on GAMLSS is published and two others are in preparation: i) `Flexible Regression and Smoothing: Using GAMLSS in R' was published on April 2017. ii) `Distributions for Modelling Location, Scale and Shape: Using GAMLSS in R' is in draft version iii) `Generalised Additive Models for Location Scale and Shape: A Distributional Regression Approach' is on preparation. The GAMLSS article on the Journal of Statistical Software can be useful for a short introduction but a slightly out of date.

## Wellcome to the new GAMLSS website