---
title: "rpart.plot (R package): papers citing this CRAN package"
description: "5 papers in the tracked journals link to rpart.plot (R package) in their Crossref records, the first in 2017; most of them (1) in 2023."
url: https://scholar4ai.com/code/cran-rpart-plot
markdown_url: https://scholar4ai.com/code/cran-rpart-plot.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 rpart.plot (R package) in their Crossref records, the first in 2017; 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)
---

# rpart.plot (R package): papers linking to it

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

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

## Key findings

- 5 papers in the tracked journals link to rpart.plot (R package) in their Crossref records, the first in 2017; most of them (1) in 2023.
- By category: Statistics (2), AI (2) and Higher Education and Research (1); the journals with the most are Computational Statistics (1), Machine Learning (1) and Scientometrics (1).

## By year

*Papers linking to rpart.plot (R package) by publication year*

| Year | Papers |
|---|---:|
| 2017 | 1 |
| 2018 | 1 |
| 2020 | 1 |
| 2022 | 1 |
| 2023 | 1 |

## Journals

*Journals whose papers link to rpart.plot (R package) most*

| Journal | Papers |
|---|---:|
| [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 1 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |
| [Scientometrics](https://scholar4ai.com/journals/scientometrics.md) | 1 |
| [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |

## Papers

*Papers linking to rpart.plot (R package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-10-13 | [Evaluating time series forecasting models: an empirical study on performance estimation methods](https://doi.org/10.1007/s10994-020-05910-7) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Vítor Cerqueira, Luı́s Torgo, Igor Mozetič | 525 | cites it |
| 2022-07-25 | [Combining unsupervised and supervised learning techniques for enhancing the performance of functional data classifiers](https://doi.org/10.1007/s00180-022-01259-8) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Fabrizio Maturo, Rosanna Verde | 28 | cites it |
| 2017-06-02 | [Classification trees for poverty mapping](https://doi.org/10.1016/j.csda.2017.05.009) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Penny Bilton, Geoff Jones, Siva Ganesh et al. | 25 | cites it |
| 2018-12-27 | [Inducing non-orthogonal and non-linear decision boundaries in decision trees via interactive basis functions](https://doi.org/10.1016/j.eswa.2018.12.041) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Antonio Páez, Fernando A. López Hernández, Manuel Ruiz Marín et al. | 20 | cites it |
| 2023-02-20 | [Institutional drivers of research productivity: a canonical multivariate analysis of Romanian public universities](https://doi.org/10.1007/s11192-023-04655-z) | [Scientometrics](https://scholar4ai.com/journals/scientometrics.md) | Claudiu Vasile Kifor, Ana Maria Benedek, Ioan Sîrbu et al. | 9 | 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). rpart.plot (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-rpart-plot

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