---
title: "sbrl (R package): papers citing this CRAN package"
description: "4 papers in the tracked journals link to sbrl (R package) in their Crossref records, the first in 2018; most of them (1) in 2023."
url: https://scholar4ai.com/code/cran-sbrl
markdown_url: https://scholar4ai.com/code/cran-sbrl.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to sbrl (R package) in their Crossref records, the first in 2018; most of them (1) in 2023. (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)
---

# sbrl (R package): papers linking to it

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

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

## Key findings

- 4 papers in the tracked journals link to sbrl (R package) in their Crossref records, the first in 2018; most of them (1) in 2023.
- By category: AI (3) and Operations Research (1); the journals with the most are Data Mining and Knowledge Discovery (2), Mathematical Programming Computation (1) and Artificial Intelligence Review (1).

## By year

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

| Year | Papers |
|---|---:|
| 2018 | 1 |
| 2020 | 1 |
| 2022 | 1 |
| 2023 | 1 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 2 |
| [Mathematical Programming Computation](https://scholar4ai.com/journals/mathematical-programming-computation.md) | 1 |
| [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 1 |

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-06-03 | [Benchmarking and survey of explanation methods for black box models](https://doi.org/10.1007/s10618-023-00933-9) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Francesco Bodria, Fosca Giannotti, Riccardo Guidotti et al. | 277 | cites it |
| 2020-06-04 | [CHIRPS: Explaining random forest classification](https://doi.org/10.1007/s10462-020-09833-6) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Julian Hatwell, Mohamed Medhat Gaber, Raja Muhammad Atif Azad | 101 | cites it |
| 2018-09-05 | [Learning customized and optimized lists of rules with mathematical programming](https://doi.org/10.1007/s12532-018-0143-8) | [Mathematical Programming Computation](https://scholar4ai.com/journals/mathematical-programming-computation.md) | Cynthia D Rudin, Şeyda Ertekin | 49 | cites it |
| 2022-11-14 | [Stable and actionable explanations of black-box models through factual and counterfactual rules](https://doi.org/10.1007/s10618-022-00878-5) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri et al. | 39 | 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). sbrl (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-sbrl

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