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).

Science Score: 33.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
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.zenodo.json file
✓
DOI references
Found 4 DOI reference(s) in README
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Academic publication links
Links to: arxiv.org
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Committers with academic emails
2 of 13 committers (15.4%) from academic institutions
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Scientific vocabulary similarity
Low similarity (15.5%) to scientific vocabulary

Keywords

boosting-algorithms cran gamboostlss gamlss machine-learning r-language r-package variable-selection

Keywords from Contributors

gam glm mboost feature-selection hyperparameters-optimization imbalance-correction learners mlr multilabel-classification predictive-modeling

Last synced: 5 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: boost-R
Language: R
Default Branch: master
Homepage:
Size: 6.92 MB

Statistics

Stars: 26
Watchers: 10
Forks: 13
Open Issues: 9
Releases: 0

Topics

boosting-algorithms cran gamboostlss gamlss machine-learning r-language r-package variable-selection

Created over 10 years ago · Last pushed 12 months ago

Metadata Files

Readme Changelog

README.md

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.
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. (Preliminary version: ArXiv 1611.10171).

Issues & Feature Requests

For issues, bugs, feature requests etc. please use the GitHub 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")

Owner

Name: boost-R
Login: boost-R
Kind: organization

Repositories: 5
Profile: https://github.com/boost-R

Model-based boosting methods for R

GitHub Events

Total

Issue comment event: 8
Push event: 3
Pull request event: 2
Fork event: 1

Last Year

Issue comment event: 8
Push event: 3
Pull request event: 2
Fork event: 1

Committers

Last synced: over 2 years ago

All Time

Total Commits: 390
Total Committers: 13
Avg Commits per committer: 30.0
Development Distribution Score (DDS): 0.744

Past Year

Commits: 4
Committers: 2
Avg Commits per committer: 2.0
Development Distribution Score (DDS): 0.25

Top Committers

Name	Email	Commits
hofner	h**r@e**b	100
Benjamin Hofner	b**r@f**e	97
Benjamin Hofner	b**r@p**e	61
janek	j**s@w**e	49
amayr	a**r@e**b	44
mayrandy	a**r@f**e	14
sbrockhaus	s**s@s**e	9
Andreas Mayr	m**r@u**e	8
fabian-s	f**l@g**m	2
schmidm	s**m@e**b	2
ja-thomas	j****s	2
stefan7th	s**h@e**b	1
Hofner	h**e@a**e	1

Committer Domains (Top 20 + Academic)

fau.de: 2 ad.pei.de: 1 uni-bonn.de: 1 stat.uni-muenchen.de: 1 pei.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 0
Total pull requests: 1
Average time to close issues: N/A
Average time to close pull requests: about 2 months
Total issue authors: 0
Total pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 7.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 1
Average time to close issues: N/A
Average time to close pull requests: about 2 months
Issue authors: 0
Pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 7.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

Pull Request Authors

Tanzmarie (1)

Top Labels

Issue Labels

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Packages

Total packages: 1
Total downloads:
- cran 1,252 last-month
Total docker downloads: 45,717

Total dependent packages: 2
Total dependent repositories: 5
Total versions: 18
Total maintainers: 1

cran.r-project.org: gamboostLSS

Boosting Methods for 'GAMLSS'

Homepage: https://github.com/boost-R/gamboostLSS
Documentation: http://cran.r-project.org/web/packages/gamboostLSS/gamboostLSS.pdf
License: GPL-2
Latest release: 2.1-0
published 12 months ago

Versions: 18
Dependent Packages: 2
Dependent Repositories: 5
Downloads: 1,252 Last month
Docker Downloads: 45,717

Rankings

Downloads: 11.3%

Dependent repos count: 13.1%

Dependent packages count: 13.7%

Average: 15.9%

Docker downloads count: 25.7%

Maintainers (1)

benjamin.hofner@pei.de

Last synced: 6 months ago

Dependencies

DESCRIPTION cran

R >= 2.10.0 depends
mboost >= 2.8 depends
parallel * depends
stabs >= 0.5 depends
grDevices * imports
graphics * imports
stats * imports
utils * imports
BayesX * suggests
R2BayesX * suggests
gamlss * suggests
gamlss.dist * suggests
survival * suggests