Introduction

gamlss is a R package implementing the Generalised Additive Models for The package gamlss is an implementation of Rigby and Stasinopoulos (2005), Appl. Statist., 54, pp. 507-554.

There are three book available for information;

1. “Flexible Regression and Smoothing: Using GAMLSS in R” explaining how the models can be used in R.

2. “Distributions for modeling location, scale and shape: Using GAMLSS in R” explaining the explicit and generated distributions available in the package gamlss.dist

3. “Generized Additive Models for Location Scale and Shape: A distributional regression approach with applications” explaining the different method for fitting GAMLSS i.e. penalised Likelihood, Bayesian and Boosting.

More more information about books and papers related to GAMLSS can be found in https://www.gamlss.com/.

The GitHub repository is now hosted under the new gamlss-dev organization: https://github.com/gamlss-dev/gamlss/.

Version 5.4-23

• Tim Cole’s suggestion in predictAll() is added. This is to deal with the problem when mu is fixed.

• Tim Cole’s suggestion in summary()is added. This to fix the problem when y~0, (that is, when there are no df’s), to be incorporated in the summary.gamlss().

Version 5.4-21

• predict() do not print the message “new prediction”

• stepGAIC() produce less lines in the output

Version 5.4-20

• The package is now hosted on GitHub at https://github.com/gamlss-dev/gamlss/.

• Add a new prodist() method for extracting fitted (in-sample) or predicted (out-of-sample) probability distributions from gamlss models (contributed by Achim Zeileis). This enables the workflow from the distributions3 package for all distributions provided by gamlss.dist. The idea is that the distributions3 objects encapsulate all information needed to obtain moments (mean, variance, etc.), probabilities, quantiles, etc. with a unified interface. See the useR! 2022 presentation by Zeileis, Lang, and Hayes for an overview.