---
title: "rpart (R package): papers citing this CRAN package"
description: "45 papers in the tracked journals link to rpart (R package) in their Crossref records, the first in 2016; most of them (6) in 2020."
url: https://scholar4ai.com/code/cran-rpart
markdown_url: https://scholar4ai.com/code/cran-rpart.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "45 papers in the tracked journals link to rpart (R package) in their Crossref records, the first in 2016; most of them (6) in 2020. (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 (R package): papers linking to it

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

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

## Key findings

- 45 papers in the tracked journals link to rpart (R package) in their Crossref records, the first in 2016; most of them (6) in 2020.
- By category: Statistics (12), Operations Research (11) and AI (11); the journals with the most are Computational Statistics (7), Machine Learning (6) and Computational Statistics & Data Analysis (4).
- 5 of the papers build on one another: a later paper cites an earlier one that also links rpart (R package).

## By year

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

| Year | Papers |
|---|---:|
| 2016 | 2 |
| 2017 | 6 |
| 2018 | 4 |
| 2019 | 4 |
| 2020 | 6 |
| 2021 | 3 |
| 2022 | 4 |
| 2023 | 4 |
| 2024 | 5 |
| 2025 | 5 |
| 2026 | 2 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 7 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 6 |
| [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | 4 |
| [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | 3 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 3 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Optimal classification trees](https://doi.org/10.1007/s10994-017-5633-9) (Machine Learning, 2017)
  - [Sparsity in optimal randomized classification trees](https://doi.org/10.1016/j.ejor.2019.12.002) (European Journal of Operational Research, 2019) — cites the paper above
  - [Tree regression models using statistical testing and mixed integer programming](https://doi.org/10.1016/j.cie.2020.107059) (Computers & Industrial Engineering, 2020) — cites the paper above
  - [Optimal randomized classification trees](https://doi.org/10.1016/j.cor.2021.105281) (Computers & Operations Research, 2021) — cites the paper above
    - [On sparse optimal regression trees](https://doi.org/10.1016/j.ejor.2021.12.022) (European Journal of Operational Research, 2021) — cites the paper above

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2017-04-03 | [Optimal classification trees](https://doi.org/10.1007/s10994-017-5633-9) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Dimitris Bertsimas, Jack Dunn | 645 | cites it |
| 2022-03-04 | [Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features](https://doi.org/10.1007/s00180-022-01207-6) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Florian Pargent, Florian Pfisterer, Janek Thomas et al. | 171 | cites it |
| 2017-12-28 | [Instance spaces for machine learning classification](https://doi.org/10.1007/s10994-017-5629-5) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Mario Andrés Muñoz, Laura Villanova, Davaatseren Baatar et al. | 153 | cites it |
| 2017-02-03 | [RHSBoost: Improving classification performance in imbalance data](https://doi.org/10.1016/j.csda.2017.01.005) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Joonho Gong, Hyunjoong Kim | 128 | cites it |
| 2018-11-02 | [Marketing analytics: Methods, practice, implementation, and links to other fields](https://doi.org/10.1016/j.eswa.2018.11.002) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Stephen L. France, Sanjoy Ghose | 107 | cites it |
| 2020-07-02 | [Double random forest](https://doi.org/10.1007/s10994-020-05889-1) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Sunwoo Han, Hyunjoong Kim, Yung-Seop Lee | 89 | cites it |
| 2024-01-31 | [Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms](https://doi.org/10.1007/s10618-024-01002-5) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Rafael Gomes Mantovani, Tomáš Horváth, André Luis Debiaso Rossi et al. | 63 | cites it |
| 2018-11-30 | [Characterization of bicycle following and overtaking maneuvers on cycling paths](https://doi.org/10.1016/j.trc.2018.11.012) | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | Hossameldin Mohammed, Alexander York Bigazzi, Tarek Sayed | 60 | cites it |
| 2019-12-16 | [Sparsity in optimal randomized classification trees](https://doi.org/10.1016/j.ejor.2019.12.002) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río et al. | 53 | cites it |
| 2021-03-08 | [Optimal randomized classification trees](https://doi.org/10.1016/j.cor.2021.105281) | [Computers & Operations Research](https://scholar4ai.com/journals/computers-operations-research.md) | Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río et al. | 48 | cites it |
| 2017-06-19 | [OpenML: An R package to connect to the machine learning platform OpenML](https://doi.org/10.1007/s00180-017-0742-2) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Giuseppe Casalicchio, Jakob Bossek, Michel Lang et al. | 42 | cites it |
| 2024-09-12 | [On the consistency of supervised learning with missing values](https://doi.org/10.1007/s00362-024-01550-4) | [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | Julie Josse, Jacob M. Chen, Nicolas Prost et al. | 41 | cites it |
| 2019-07-01 | [Optimizing real-world factory flows using aggregated discrete event simulation modelling](https://doi.org/10.1007/s10696-019-09362-7) | [Flexible Services and Manufacturing Journal](https://scholar4ai.com/journals/flexible-services-and-manufacturing-journal.md) | Simon Lidberg, Tehseen Aslam, Leif Pehrsson et al. | 36 | cites it |
| 2020-04-18 | [BEST: a decision tree algorithm that handles missing values](https://doi.org/10.1007/s00180-020-00987-z) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Cédric Beaulac, Jeffrey S. Rosenthal | 28 | 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 |
| 2016-12-30 | [Confidence curves: an alternative to null hypothesis significance testing for the comparison of classifiers](https://doi.org/10.1007/s10994-016-5612-6) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Daniel Berrar | 27 | cites it |
| 2022-11-03 | [Feature selection algorithms in generalized additive models under concurvity](https://doi.org/10.1007/s00180-022-01292-7) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | László Kovács | 27 | 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 |
| 2020-07-09 | [The impact of operating environment on efficiency of public libraries](https://doi.org/10.1007/s10100-020-00696-4) | [Central European Journal of Operations Research](https://scholar4ai.com/journals/central-european-journal-of-operations-research.md) | Vladimír Holý | 16 | cites it |
| 2023-01-09 | [Sparse oblique decision trees: a tool to understand and manipulate neural net features](https://doi.org/10.1007/s10618-022-00892-7) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán, Arman Serikuly Zharmagambetov | 16 | cites it |
| 2021-12-18 | [On sparse optimal regression trees](https://doi.org/10.1016/j.ejor.2021.12.022) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río et al. | 15 | cites it |
| 2024-04-10 | [Lost in the Forest: Encoding categorical variables and the absent levels problem](https://doi.org/10.1007/s10618-024-01019-w) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Helen L. Smith, Patrick J. Biggs, Nigel Peter French et al. | 15 | cites it |
| 2020-12-18 | [Tree regression models using statistical testing and mixed integer programming](https://doi.org/10.1016/j.cie.2020.107059) | [Computers & Industrial Engineering](https://scholar4ai.com/journals/computers-industrial-engineering.md) | Ioannis Gkioulekas, Lazaros G. Papageorgiou | 14 | cites it |
| 2019-03-12 | [Uncertainty and energy-sector equity returns in Iran: a Bayesian and quasi-Monte Carlo time-varying analysis](https://doi.org/10.1186/s40854-019-0128-2) | [Financial Innovation](https://scholar4ai.com/journals/financial-innovation.md) | Babak Fazelabdolabadi | 13 | cites it |
| 2020-02-28 | [Fast and Frugal heuristics augmented: When machine learning quantifies Bayesian uncertainty](https://doi.org/10.1016/j.jbef.2020.100293) | [Journal of Behavioral and Experimental Finance](https://scholar4ai.com/journals/journal-of-behavioral-and-experimental-finance.md) | Grégory Gadzinski, Alessio Castello | 11 | cites it |
| 2024-12-04 | [Organizational ambidexterity and student achievement: Do knowledge exploration and exploitation in schools make a difference?](https://doi.org/10.1016/j.jik.2024.100636) | [Journal of Innovation & Knowledge](https://scholar4ai.com/journals/journal-of-innovation-knowledge.md) | Marcus Pietsch, Burak Aydın, Carmen Montecinos et al. | 11 | cites it |
| 2018-01-15 | [Guidelines for assessing the value of a predictive algorithm: a case study](https://doi.org/10.1057/s41270-017-0027-1) | [Journal of Marketing Analytics](https://scholar4ai.com/journals/journal-of-marketing-analytics.md) | Ossi Ylijoki | 10 | cites it |
| 2019-02-16 | [An exploratory analysis approach for understanding variation in stochastic textured surfaces](https://doi.org/10.1016/j.csda.2019.01.019) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Anh Tuan Bui, Daniel W. Apley | 10 | cites it |
| 2020-05-15 | [Analytics for labor planning in systems with load-dependent service times](https://doi.org/10.1016/j.ejor.2020.04.036) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Dmitry Smirnov, Arnd Huchzermeier | 10 | cites it |
| 2016-10-27 | [Deriving optimal data-analytic regimes from benchmarking studies](https://doi.org/10.1016/j.csda.2016.10.016) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Lisa L. Doove, Tom F. Wilderjans, Antonio Calcagnì et al. | 9 | 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 |
| 2023-08-02 | [Rating and perceived helpfulness in a bipartite network of online product reviews](https://doi.org/10.1007/s10660-023-09725-1) | [Electronic Commerce Research](https://scholar4ai.com/journals/electronic-commerce-research.md) | Pedro Campos, Eva Pinto, Ana Isabel Torres | 9 | cites it |
| 2021-01-12 | [Technology Integration and Analysis Using Boosting and Ensemble](https://doi.org/10.3390/joitmc7010027) | [Journal of Open Innovation Technology Market and Complexity](https://scholar4ai.com/journals/journal-of-open-innovation-technology-market-and-complexity.md) | Sunghae Jun | 8 | cites it |
| 2024-03-27 | [Using Decision Trees to Predict Insolvency in Spanish SMEs: Is Early Warning Possible?](https://doi.org/10.1007/s10614-024-10586-5) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Andrés Navarro Galera, Juan Lara‐Rubio, Pavel Novoa Hernández et al. | 8 | cites it |
| 2022-01-24 | [Models under which random forests perform badly; consequences for applications](https://doi.org/10.1007/s00180-021-01182-4) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | José A. Ferreira | 7 | cites it |
| 2025-07-02 | [Transparency of combinatorial optimisations via machine learning and explainable AI](https://doi.org/10.1007/s10479-025-06684-8) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Wolfgang Garn, Mehrdad Amirghasemi | 5 | cites it |
| 2025-03-25 | [DatRel: a noise-tolerant data relocation approach for effective synthetic data generation in imbalanced classifiers](https://doi.org/10.1007/s10994-025-06755-8) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Fatih Sağlam | 4 | cites it |
| 2017-01-11 | [Big Data: from collection to visualization](https://doi.org/10.1007/s10994-016-5622-4) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Mohammed Ghesmoune, Hanane Azzag, Salima Benbernou et al. | 3 | cites it |
| 2025-09-02 | [A holistic approach to interpretable modelling and forecasting of human mortality by gender and country](https://doi.org/10.1007/s10479-025-06779-2) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Ana Debón, Steven Haberman, Gabriella Piscopo | 3 | cites it |
| 2023-05-22 | [Tree-based boosting with functional data](https://doi.org/10.1007/s00180-023-01364-2) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Xiaomeng Ju, Matías Salibián‐Barrera | 1 | cites it |
| 2025-11-10 | [Individualized Treatment Rules Based on Cost-Effectiveness Criteria in Microsimulations](https://doi.org/10.1007/s40273-025-01562-4) | [PharmacoEconomics](https://scholar4ai.com/journals/pharmacoeconomics.md) | Niklaus Meier, Ana Cecilia Quiroga Gutiérrez, Mark Pletscher et al. | 1 | cites it |
| 2025-08-06 | [Redundancy and delimitation of the Pareto front](https://doi.org/10.1007/s12351-025-00966-6) | [Operational Research](https://scholar4ai.com/journals/operational-research.md) | Christina D. Nikolakakou | 0 | cites it |
| 2026-06-20 | [On the determinants of intensity and duration in institutional long-term care in Switzerland: New insights from random forest modeling](https://doi.org/10.1016/j.insmatheco.2026.103272) | [Insurance Mathematics and Economics](https://scholar4ai.com/journals/insurance-mathematics-and-economics.md) | Lucien Lorenz, Joël Wagner | 0 | cites it |
| 2026-07-16 | [Conservation outcomes across protected area strategies: Insights from a longitudinal analysis of the Brazilian National System of Conservation Units](https://doi.org/10.1016/j.landusepol.2026.108226) | [Land Use Policy](https://scholar4ai.com/journals/land-use-policy.md) | Clarisse Carmo, Alberto Fonseca | 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). rpart (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-rpart

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