---
title: "rmarkdown (R package): papers citing this CRAN package"
description: "6 papers in the tracked journals link to rmarkdown (R package) in their Crossref records, the first in 2018; most of them (2) in 2019."
url: https://scholar4ai.com/code/cran-rmarkdown
markdown_url: https://scholar4ai.com/code/cran-rmarkdown.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to rmarkdown (R package) in their Crossref records, the first in 2018; 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)
---

# rmarkdown (R package): papers linking to it

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

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

## Key findings

- 6 papers in the tracked journals link to rmarkdown (R package) in their Crossref records, the first in 2018; most of them (2) in 2019.
- By category: AI (4), Economics (1) and Statistics (1); the journals with the most are Neural Computing and Applications (2), Energy Policy (1) and Statistical Papers (1).

## By year

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

| Year | Papers |
|---|---:|
| 2018 | 1 |
| 2019 | 2 |
| 2020 | 1 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | 1 |
| [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | 1 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 |
| [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | 1 |

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-07-06 | [Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms](https://doi.org/10.1007/s00521-020-05172-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis | 177 | cites it |
| 2019-03-06 | [Onshore wind and the likelihood of planning acceptance: Learning from a Great Britain context](https://doi.org/10.1016/j.enpol.2019.01.002) | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | Michael Harper, Ben Anderson, Patrick A.B. James et al. | 61 | cites it |
| 2019-04-08 | [Beyond lexical frequencies: using R for text analysis in the digital humanities](https://doi.org/10.1007/s10579-019-09456-6) | [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | Taylor Arnold, Nicolas Ballier, Paula Lissón et al. | 10 | cites it |
| 2018-05-30 | [A web-oriented expert system for planning hurdles race training programmes](https://doi.org/10.1007/s00521-018-3559-1) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Krzysztof Przednówek, Krzysztof Wiktorowicz, Tomasz Krzeszowski et al. | 7 | cites it |
| 2025-03-05 | [Deep Huber quantile regression networks](https://doi.org/10.1016/j.neunet.2025.107364) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Hristos Tyralis, Georgia Papacharalampous, Nilay Doğulu et al. | 7 | cites it |
| 2026-04-11 | [Cor: an R package for optimal subset selection in distributed estimation](https://doi.org/10.1007/s00362-026-01830-1) | [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | Di Chang, Guangbao Guo, Lixing Zhu | 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). rmarkdown (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-rmarkdown

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