---
title: "mboost (R package): papers citing this CRAN package"
description: "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."
url: https://scholar4ai.com/code/cran-mboost
markdown_url: https://scholar4ai.com/code/cran-mboost.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# mboost (R package): papers linking to it

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.

- CRAN
- https://CRAN.R-project.org/package=mboost

All repositories: https://scholar4ai.com/code.md

## 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

*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](https://scholar4ai.com/journals/statistics-and-computing.md) | 3 |
| [Letters in Spatial and Resource Sciences](https://scholar4ai.com/journals/letters-in-spatial-and-resource-sciences.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Financial Innovation](https://scholar4ai.com/journals/financial-innovation.md) | 1 |
| [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates](https://doi.org/10.1007/s11222-017-9754-6) (Statistics and Computing, 2017)
  - [Trimming stability selection increases variable selection robustness](https://doi.org/10.1007/s10994-023-06384-z) (Machine Learning, 2023) — cites the paper above

## Papers

*Papers linking to mboost (R package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2016-12-28 | [D-vine copula based quantile regression](https://doi.org/10.1016/j.csda.2016.12.009) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Daniel Kraus, Claudia Czado | 183 | cites it |
| 2017-05-11 | [Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates](https://doi.org/10.1007/s11222-017-9754-6) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 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](https://doi.org/10.1186/s40854-024-00625-3) | [Financial Innovation](https://scholar4ai.com/journals/financial-innovation.md) | 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](https://doi.org/10.1007/s00180-017-0773-8) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 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](https://doi.org/10.1007/s11222-022-10138-7) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 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](https://doi.org/10.1007/s10489-019-01509-1) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Marcin Mirończuk, Jarosław Protasiewicz | 7 | cites it |
| 2023-10-04 | [Trimming stability selection increases variable selection robustness](https://doi.org/10.1007/s10994-023-06384-z) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Tino Werner | 7 | cites it |
| 2025-04-29 | [Gradient boosting for generalised additive mixed models](https://doi.org/10.1007/s11222-025-10612-y) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 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](https://doi.org/10.1007/s12076-021-00268-3) | [Letters in Spatial and Resource Sciences](https://scholar4ai.com/journals/letters-in-spatial-and-resource-sciences.md) | 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).