GAMLSS Modeling with Advanced Flexible Infrastructures
Description
Next generation infrastructure for generalized additive models for location, scale, and shape (GAMLSS) and distributional regression more generally. The package provides a fresh reimplementation of the classic ‘gamlss’ package while being more modular and facilitating the creation of advanced terms and models.
Details
gamlss2 fits an additive predictor for each parameter of a response distribution, using linear terms, smooth terms from mgcv, and user-defined model terms (see specials). Response distributions are specified through gamlss2.family objects, including families with more than four parameters. Fitting algorithms can be selected through gamlss2_control. Fitted models provide methods for summaries, prediction, plotting, and diagnostics. Predictions can be returned as distributions3 objects for evaluating fitted distributions (see predict.gamlss2).
References
To cite gamlss2 in publications use:
Umlauf N, Stasinopoulos M, De Bastiani F, Heller G, Kneib T, Mayr A, Rigby R, Stauffer R, Zeileis A (2025). “gamlss2: The Next Generation of Distributional Regression”. In O’Sullivan D, Sweeney J, Moghaddam S, Burke K (eds.), Proceedings of the 39th International Workshop on Statistical Modelling, 312–317. Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland. https://iwsm2025.ie/wp-content/uploads/2025/07/IWSM2025_Limerick_Proceedings.pdf
For the underlying methods cite:
Rigby RA, Stasinopoulos DM (2005). “Generalized Additive Models for Location, Scale and Shape (with Discussion)”. Journal of the Royal Statistical Society, Series C (Applied Statistics), 54(3), 507–554. doi:10.1111/j.1467-9876.2005.00510.x
See Also
gamlss2, fake_formula