randomForestSRC (R package): papers linking to it

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

← Code and data repositories cited in research papers

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.

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#

20192026

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	1
Statistics and Computing	1
Expert Systems with Applications	1
Pattern Recognition	1
Annals of Operations Research	1

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	Pattern Recognition	Robert C. O’Brien, Hemant Ishwaran	186	cites it
2019-06-06	Evaluating multi-label classifiers and recommender systems in the financial service sector	European Journal of Operational Research	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	Statistics and Computing	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	Expert Systems with Applications	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	Annals of Operations Research	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

@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}
}

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