---
title: "randomForestSRC (R package): papers citing it"
description: "5 papers in the tracked journals link to randomForestSRC (R package) in their Crossref records, the first in 2019; most of them (2) in 2019."
url: https://scholar4ai.com/code/cran-randomforestsrc
markdown_url: https://scholar4ai.com/code/cran-randomforestsrc.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to randomForestSRC (R package) in their Crossref records, the first in 2019; most of them (2) in 2019. (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)
---

# randomForestSRC (R package): papers linking to it

Research papers in the 480 tracked journals that link to randomForestSRC (R package) (https://CRAN.R-project.org/package=randomForestSRC), 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=randomForestSRC

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

## Key findings

- 5 papers in the tracked journals link to randomForestSRC (R package) in their Crossref records, the first in 2019; most of them (2) in 2019.
- By category: Operations Research (2), AI (2) and Statistics (1); the journals with the most are European Journal of Operational Research (1), Statistics and Computing (1) and Expert Systems with Applications (1).

## By year

*Papers linking to randomForestSRC (R package) by publication year*

| Year | Papers |
|---|---:|
| 2019 | 2 |
| 2021 | 1 |
| 2022 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to randomForestSRC (R package) most*

| Journal | Papers |
|---|---:|
| [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | 1 |
| [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 1 |
| [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | 1 |

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2019-01-29 | [A random forests quantile classifier for class imbalanced data](https://doi.org/10.1016/j.patcog.2019.01.036) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Robert C. O’Brien, Hemant Ishwaran | 186 | cites it |
| 2019-06-06 | [Evaluating multi-label classifiers and recommender systems in the financial service sector](https://doi.org/10.1016/j.ejor.2019.05.037) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Matthias Bogaert, Justine Lootens, Dirk Van den Poel et al. | 55 | 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 |
| 2021-04-01 | [Automatic mass spectra recognition for Ultra High Vacuum systems using multilabel classification](https://doi.org/10.1016/j.eswa.2021.114959) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Fernando Mateo, Juan José Garcés-Iniesta, Berthold Jenninger et al. | 6 | cites it |
| 2026-09-01 | [Enhancing data envelopment analysis: a comprehensive calibration with random forests](https://doi.org/10.1007/s10479-026-07403-7) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Zhichao Wang | 0 | 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). randomForestSRC (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-randomforestsrc

```bibtex
@misc{scholar4ai-code-cran-randomforestsrc,
  author = {{Scholar4AI}},
  title = {randomForestSRC (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-randomforestsrc}},
  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/).