---
title: "SuperLearner (R package): papers citing this CRAN package"
description: "6 papers in the tracked journals link to SuperLearner (R package) in their Crossref records, the first in 2018; most of them (1) in 2026."
url: https://scholar4ai.com/code/cran-superlearner
markdown_url: https://scholar4ai.com/code/cran-superlearner.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 SuperLearner (R package) in their Crossref records, the first in 2018; most of them (1) in 2026. (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)
---

# SuperLearner (R package): papers linking to it

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

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

## Key findings

- 6 papers in the tracked journals link to SuperLearner (R package) in their Crossref records, the first in 2018; most of them (1) in 2026.
- By category: Statistics (3) and AI (3); the journals with the most are Expert Systems with Applications (2), Computational Statistics (1) and Journal of the Korean Statistical Society (1).

## By year

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

| Year | Papers |
|---|---:|
| 2018 | 1 |
| 2020 | 1 |
| 2022 | 1 |
| 2023 | 1 |
| 2024 | 1 |
| 2026 | 1 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 1 |
| [Journal of the Korean Statistical Society](https://scholar4ai.com/journals/journal-of-the-korean-statistical-society.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | 1 |

## Papers

*Papers linking to SuperLearner (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 |
| 2022-01-01 | [Predictive Model of Employee Attrition Based on Stacking Ensemble Learning](https://doi.org/10.1016/j.eswa.2022.119364) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Doohee Chung, Jinseop Yun, Jeha Lee et al. | 100 | cites it |
| 2023-07-13 | [A hybrid machine learning framework for forecasting house price](https://doi.org/10.1016/j.eswa.2023.120981) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Choujun Zhan, Yonglin Liu, Zeqiong Wu et al. | 51 | cites it |
| 2018-09-14 | [When and when not to use optimal model averaging](https://doi.org/10.1007/s00362-018-1048-3) | [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | Michael Schomaker, Christian Heumann | 7 | cites it |
| 2024-09-23 | [Forecasting the cost of drought events in France by Super Learning from a short time series of many slightly dependent data](https://doi.org/10.1007/s00180-024-01549-3) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Geoffrey Ecoto, Aurélien Bibaut, Antoine Chambaz | 1 | cites it |
| 2026-09-23 | [Robust estimation of individualized quantile treatment effects in high dimensions](https://doi.org/10.1007/s42952-026-00391-1) | [Journal of the Korean Statistical Society](https://scholar4ai.com/journals/journal-of-the-korean-statistical-society.md) | Seohyeon Park, Minji Sung, Sangbum Choi | 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). SuperLearner (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-superlearner

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