mboost (R package): papers linking to it

CRAN · CRAN.R-project.org/package=mboost

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to mboost (R package) (https://CRAN.R-project.org/package=mboost), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

9 papers in the tracked journals link to mboost (R package) in their Crossref records, the first in 2016; most of them (2) in 2017.
By category: Statistics (5), AI (2) and Economics (1); the journals with the most are Statistics and Computing (3), Letters in Spatial and Resource Sciences (1) and Applied Intelligence (1).
2 of the papers build on one another: a later paper cites an earlier one that also links mboost (R package).

By year#

20162025

Papers linking to mboost (R package) by publication year
Year	Papers
2016	1
2017	2
2019	1
2021	1
2022	1
2023	1
2024	1
2025	1

Journals#

Journals whose papers link to mboost (R package) most
Journal	Papers
Statistics and Computing	3
Letters in Spatial and Resource Sciences	1
Applied Intelligence	1
Financial Innovation	1
Computational Statistics & Data Analysis	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates Statistics and Computing, 2017
- Trimming stability selection increases variable selection robustness Machine Learning, 2023 — cites the paper above

Papers linking to mboost (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2016-12-28	D-vine copula based quantile regression	Computational Statistics & Data Analysis	Daniel Kraus, Claudia Czado	183	cites it
2017-05-11	Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates	Statistics and Computing	Janek Thomas, Andreas J. Mayr, Bernd Bischl et al.	74	cites it
2024-03-01	Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption	Financial Innovation	Blanco-Oliver Antonio, Lara-Rubio Juan, Irimia-Diéguez Ana et al.	29	cites it
2017-11-28	On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models	Computational Statistics	Heidi Seibold, Christoph Bernau, Anne‐Laure Boulesteix et al.	27	cites it
2022-10-27	Representative random sampling: an empirical evaluation of a novel bin stratification method for model performance estimation	Statistics and Computing	Michael C. Rendleman, Brian J. Smith, Guadalupe M. Canahuate et al.	10	cites it
2019-06-22	Recognising innovative companies by using a diversified stacked generalisation method for website classification	Applied Intelligence	Marcin Mirończuk, Jarosław Protasiewicz	7	cites it
2023-10-04	Trimming stability selection increases variable selection robustness	Machine Learning	Tino Werner	7	cites it
2025-04-29	Gradient boosting for generalised additive mixed models	Statistics and Computing	Lars Knieper, Torsten Hothorn, Elisabeth Bergherr et al.	3	cites it
2021-03-25	Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul	Letters in Spatial and Resource Sciences	Guilherme Schultz Lindenmeyer, Pedro Pablo Skorin, Hudson S. Torrent	1	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). mboost (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-mboost

@misc{scholar4ai-code-cran-mboost,
  author = {{Scholar4AI}},
  title = {mboost (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-mboost}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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