gamlss2/README.qmd at main · gamlss-dev/gamlss2 · GitHub

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<!-- README.md is generated from README.qmd via: quarto render README.qmd --to gfm -->

```{r figures}
#| echo: false
if(!file.exists("figures")) {
  dir.create("figures")
}
knitr::opts_chunk$set(fig.path = "figures/")
```

<img src="https://gamlss-dev.github.io/gamlss2/gamlss2.png" align="right" alt="gamlss2 logo" width="120" />

# 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`](https://cran.r-project.org/package=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`](https://cran.r-project.org/package=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()`](https://gamlss-dev.github.io/gamlss2/man/gamlss2.html).
* The default optimizer functions is [`RS()`](https://gamlss-dev.github.io/gamlss2/man/RS_CG.html). Optimizer functions can be exchanged.
* Most important methods: `summary()`, [`plot()`](https://gamlss-dev.github.io/gamlss2/man/plots.html), [`predict()`](https://gamlss-dev.github.io/gamlss2/man/predict.gamlss2.html).
* Easy development of new family objects, see [`?gamlss2,family`](https://gamlss-dev.github.io/gamlss2/man/gamlss2.family.html).
* User-specific "special" terms are possible, see [`?special_terms`](https://gamlss-dev.github.io/gamlss2/man/special_terms.html).

For examples, please visit the manual pages.

```{r help}
#| eval: false
help(package = "gamlss2")
```

## Installation

The development version of `gamlss2` can be installed via

```{r installation, eval = FALSE}
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](https://www.gnu.org/licenses/gpl-3.0.html)
or [version 2](https://www.gnu.org/licenses/old-licenses/gpl-2.0.html)

## Vignettes

* The basic workflow is illustrated in the vignette [First Steps](https://gamlss-dev.github.io/gamlss2/vignettes/firststeps.html).
* To learn about distribution families in `gamlss2`, see the vignette
  [Families](https://gamlss-dev.github.io/gamlss2/vignettes/families.html),
  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](https://gamlss-dev.github.io/gamlss2/vignettes/specials.html)
  provides a concise introduction.
* Model assessment and calibration using the `topmodels` package are
  discussed in the
  [Topmodels](https://gamlss-dev.github.io/gamlss2/vignettes/topmodels.html)
  vignette.