https://github.com/boost-R/gamboostLSS
Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).
https://github.com/boost-R/gamboostLSS
boosting-algorithms cran gamboostlss gamlss machine-learning r-language r-package variable-selection
Last synced: about 2 months ago
JSON representation
Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).
- Host: GitHub
- URL: https://github.com/boost-R/gamboostLSS
- Owner: boost-R
- Created: 2015-07-02T15:40:34.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2026-01-26T18:03:24.000Z (about 2 months ago)
- Last Synced: 2026-01-27T06:00:35.350Z (about 2 months ago)
- Topics: boosting-algorithms, cran, gamboostlss, gamlss, machine-learning, r-language, r-package, variable-selection
- Language: R
- Homepage:
- Size: 6.96 MB
- Stars: 27
- Watchers: 9
- Forks: 13
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog
Awesome Lists containing this project
- awesome-gradient-boosting-machines - gamboostLSS - R package for boosting GAMLSS (Generalized Additive Models for Location, Scale, Shape). High-dimensional distributional regression with gradient boosting. [[JSS Paper](https://www.jstatsoft.org/article/view/v074i01)] ⭐ 70+ (Implementations / Other Frameworks)
README
gamboostLSS
===========
[](https://app.travis-ci.com/boost-R/gamboostLSS)
[](https://ci.appveyor.com/project/hofnerb/gamboostlss-s2whe/branch/master)
[](https://CRAN.R-project.org/package=gamboostLSS)
[](https://coveralls.io/github/boost-R/gamboostLSS?branch=master)
[](https://CRAN.R-project.org/package=gamboostLSS)
`gamboostLSS` implements boosting algorithms for fitting generalized linear,
additive and interaction models for to potentially high-dimensional data.
Instead of modeling only the mean, `gamboostLSS` enables the user to model
various distribution parameters such as location, scale and shape at the same
time (hence the name GAMLSS, generalized additive models for location, scale and
shape).
## Using gamboostLSS
- For installation instructions see below.
- Instructions on how to use `gamboostLSS` can be found in the
[gamboostLSS tutorial](https://www.jstatsoft.org/article/view/v074i01).
- Details on the noncyclical fitting method can be found in
Thomas, J., Mayr, A., Bischl, B., Schmid, M., Smith, A., and Hofner, B. (2018),
Gradient boosting for distributional regression - faster tuning and improved
variable selection via noncyclical updates.
*Statistics and Computing*. 28: 673-687. DOI [10.1007/s11222-017-9754-6](http://dx.doi.org/10.1007/s11222-017-9754-6).
(Preliminary version: [ArXiv 1611.10171](https://arxiv.org/abs/1611.10171)).
## Issues & Feature Requests
For issues, bugs, feature requests etc. please use the [GitHub Issues](https://github.com/boost-R/gamboostLSS/issues).
## Installation
- Current version (from CRAN):
```
install.packages("gamboostLSS")
```
- Latest **patch version** (patched version of CRAN package; under development) from GitHub:
```
library("devtools")
install_github("boost-R/gamboostLSS")
library("gamboostLSS")
```
- Latest **development version** (version with new features; under development) from GitHub:
```
library("devtools")
install_github("boost-R/gamboostLSS", ref = "devel")
library("gamboostLSS")
```
To be able to use the `install_github()` command, one needs to install `devtools` first:
```
install.packages("devtools")
```