gamlss2: Infrastructure for Flexible Distributional Regression

Overview

The primary purpose of this package is to facilitate the creation of advanced infrastructures designed to enhance the GAMLSS modeling framework. Notably, the gamlss2 package represents a significant overhaul of its predecessor, gamlss, with a key emphasis on improving estimation speed and incorporating more flexible infrastructures. These enhancements enable the seamless integration of various algorithms into GAMLSS, including gradient boosting, Bayesian estimation, regression trees, and forests, fostering a more versatile and powerful modeling environment.

Moreover, the package expands its compatibility by supporting all model terms from the base R mgcv package. Additionally, the gamlss2 package introduces the capability to accommodate more than four parameter families. Essentially, this means that users can now specify any type of model using these new infrastructures, making the package highly flexible and accommodating to a wide range of modeling requirements.

• The main model function is gamlss2().
• The default optimizer functions is RS(). Optimizer functions can be exchanged.
• Most important methods: summary(), plot(), predict().
• Easy development of new family objects, see ?gamlss2,family.
• User-specific “special” terms are possible, see ?special_terms.

For examples, please visit the manual pages.

help(package = "gamlss2")

Installation

The development version of gamlss2 can be installed via

install.packages("gamlss2",
  repos = c("https://gamlss-dev.R-universe.dev",
            "https://cloud.R-project.org"))

Licence

The package is available under the General Public License version 3 or version 2

Vignettes

• The basic workflow is illustrated in the vignette First Steps.
• To learn about distribution families in gamlss2, see the vignette Families, which provides an overview and examples on how to implement custom families.
• For information on using and setting up new special model terms (e.g., neural networks), the vignette Specials provides a concise introduction.
• Model assessment and calibration using the topmodels package are discussed in the Topmodels vignette.