rpart (R package): papers linking to it

← Code and data repositories cited in research papers

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.

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#

20162026

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	7
Machine Learning	6
Computational Statistics & Data Analysis	4
European Journal of Operational Research	3
Data Mining and Knowledge Discovery	3

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Optimal classification trees Machine Learning, 2017
- Sparsity in optimal randomized classification trees European Journal of Operational Research, 2019 — cites the paper above
- Tree regression models using statistical testing and mixed integer programming Computers & Industrial Engineering, 2020 — cites the paper above
- Optimal randomized classification trees Computers & Operations Research, 2021 — cites the paper above
  - On sparse optimal regression trees European Journal of Operational Research, 2021 — cites the paper above

Papers linking to rpart (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2017-04-03	Optimal classification trees	Machine Learning	Dimitris Bertsimas, Jack Dunn	645	cites it
2022-03-04	Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features	Computational Statistics	Florian Pargent, Florian Pfisterer, Janek Thomas et al.	171	cites it
2017-12-28	Instance spaces for machine learning classification	Machine Learning	Mario Andrés Muñoz, Laura Villanova, Davaatseren Baatar et al.	153	cites it
2017-02-03	RHSBoost: Improving classification performance in imbalance data	Computational Statistics & Data Analysis	Joonho Gong, Hyunjoong Kim	128	cites it
2018-11-02	Marketing analytics: Methods, practice, implementation, and links to other fields	Expert Systems with Applications	Stephen L. France, Sanjoy Ghose	107	cites it
2020-07-02	Double random forest	Machine Learning	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	Data Mining and Knowledge Discovery	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	Transportation Research Part C Emerging Technologies	Hossameldin Mohammed, Alexander York Bigazzi, Tarek Sayed	60	cites it
2019-12-16	Sparsity in optimal randomized classification trees	European Journal of Operational Research	Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río et al.	53	cites it
2021-03-08	Optimal randomized classification trees	Computers & Operations Research	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	Computational Statistics	Giuseppe Casalicchio, Jakob Bossek, Michel Lang et al.	42	cites it
2024-09-12	On the consistency of supervised learning with missing values	Statistical Papers	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	Flexible Services and Manufacturing Journal	Simon Lidberg, Tehseen Aslam, Leif Pehrsson et al.	36	cites it
2020-04-18	BEST: a decision tree algorithm that handles missing values	Computational Statistics	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	Computational Statistics	Fabrizio Maturo, Rosanna Verde	28	cites it
2016-12-30	Confidence curves: an alternative to null hypothesis significance testing for the comparison of classifiers	Machine Learning	Daniel Berrar	27	cites it
2022-11-03	Feature selection algorithms in generalized additive models under concurvity	Computational Statistics	László Kovács	27	cites it
2017-06-02	Classification trees for poverty mapping	Computational Statistics & Data Analysis	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	Expert Systems with Applications	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	Central European Journal of Operations Research	Vladimír Holý	16	cites it
2023-01-09	Sparse oblique decision trees: a tool to understand and manipulate neural net features	Data Mining and Knowledge Discovery	Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán, Arman Serikuly Zharmagambetov	16	cites it
2021-12-18	On sparse optimal regression trees	European Journal of Operational Research	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	Data Mining and Knowledge Discovery	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	Computers & Industrial Engineering	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	Financial Innovation	Babak Fazelabdolabadi	13	cites it
2020-02-28	Fast and Frugal heuristics augmented: When machine learning quantifies Bayesian uncertainty	Journal of Behavioral and Experimental Finance	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?	Journal of Innovation & Knowledge	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	Journal of Marketing Analytics	Ossi Ylijoki	10	cites it
2019-02-16	An exploratory analysis approach for understanding variation in stochastic textured surfaces	Computational Statistics & Data Analysis	Anh Tuan Bui, Daniel W. Apley	10	cites it
2020-05-15	Analytics for labor planning in systems with load-dependent service times	European Journal of Operational Research	Dmitry Smirnov, Arnd Huchzermeier	10	cites it
2016-10-27	Deriving optimal data-analytic regimes from benchmarking studies	Computational Statistics & Data Analysis	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	Scientometrics	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	Electronic Commerce Research	Pedro Campos, Eva Pinto, Ana Isabel Torres	9	cites it
2021-01-12	Technology Integration and Analysis Using Boosting and Ensemble	Journal of Open Innovation Technology Market and Complexity	Sunghae Jun	8	cites it
2024-03-27	Using Decision Trees to Predict Insolvency in Spanish SMEs: Is Early Warning Possible?	Computational Economics	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	Computational Statistics	José A. Ferreira	7	cites it
2025-07-02	Transparency of combinatorial optimisations via machine learning and explainable AI	Annals of Operations Research	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	Machine Learning	Fatih Sağlam	4	cites it
2017-01-11	Big Data: from collection to visualization	Machine Learning	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	Annals of Operations Research	Ana Debón, Steven Haberman, Gabriella Piscopo	3	cites it
2023-05-22	Tree-based boosting with functional data	Computational Statistics	Xiaomeng Ju, Matías Salibián‐Barrera	1	cites it
2025-11-10	Individualized Treatment Rules Based on Cost-Effectiveness Criteria in Microsimulations	PharmacoEconomics	Niklaus Meier, Ana Cecilia Quiroga Gutiérrez, Mark Pletscher et al.	1	cites it
2025-08-06	Redundancy and delimitation of the Pareto front	Operational Research	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	Insurance Mathematics and Economics	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	Land Use Policy	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

@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}
}

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