--- title: "C50 (R package): papers citing this CRAN package" description: "11 papers in the tracked journals link to C50 (R package) in their Crossref records, the first in 2017; most of them (3) in 2019." url: https://scholar4ai.com/code/cran-c50 markdown_url: https://scholar4ai.com/code/cran-c50.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "11 papers in the tracked journals link to C50 (R package) in their Crossref records, the first in 2017; most of them (3) in 2019. (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) --- # C50 (R package): papers linking to it Research papers in the 480 tracked journals that link to C50 (R package) (https://CRAN.R-project.org/package=C50), 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=C50 All repositories: https://scholar4ai.com/code.md ## Key findings - 11 papers in the tracked journals link to C50 (R package) in their Crossref records, the first in 2017; most of them (3) in 2019. - By category: AI (4), Statistics (3) and Operations Research (2); the journals with the most are Computational Statistics (3), Computational Economics (1) and Journal of the Academy of Marketing Science (1). ## By year *Papers linking to C50 (R package) by publication year* | Year | Papers | |---|---:| | 2017 | 2 | | 2018 | 3 | | 2019 | 3 | | 2020 | 1 | | 2021 | 1 | | 2025 | 1 | ## Journals *Journals whose papers link to C50 (R package) most* | Journal | Papers | |---|---:| | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 3 | | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | 1 | | [Journal of the Academy of Marketing Science](https://scholar4ai.com/journals/journal-of-the-academy-of-marketing-science.md) | 1 | | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 | | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 | ## Papers *Papers linking to C50 (R package), most cited first* | Published | Title | Journal | Authors | Citations | Link | |---|---|---|---|---:|---| | 2021-10-11 | [Revisiting Gaussian copulas to handle endogenous regressors](https://doi.org/10.1007/s11747-021-00805-y) | [Journal of the Academy of Marketing Science](https://scholar4ai.com/journals/journal-of-the-academy-of-marketing-science.md) | Jan-Michael Becker, Dorian Proksch, Christian M. Ringle | 309 | cites it | | 2019-01-29 | [A random forests quantile classifier for class imbalanced data](https://doi.org/10.1016/j.patcog.2019.01.036) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Robert C. O’Brien, Hemant Ishwaran | 186 | cites it | | 2018-01-01 | [Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection](https://doi.org/10.1007/s10614-018-9864-z) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Yan Zhang, Peter Trubey | 102 | cites it | | 2017-05-04 | [Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method](https://doi.org/10.1016/j.knosys.2017.05.003) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Ligang Zhou, Yain‐Whar Si, Hamido Fujita | 50 | 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 | | 2019-06-08 | [Predicting missing values: a comparative study on non-parametric approaches for imputation](https://doi.org/10.1007/s00180-019-00900-3) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Burim Ramosaj, Markus Pauly | 47 | 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 | | 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 | | 2018-03-18 | [Predicting interpurchase time in a retail environment using customer-product networks: An empirical study and evaluation](https://doi.org/10.1016/j.eswa.2018.03.016) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Jasmien Lismont, Sudha Ram, Jan Vanthienen et al. | 15 | cites it | | 2019-06-22 | [Recognising innovative companies by using a diversified stacked generalisation method for website classification](https://doi.org/10.1007/s10489-019-01509-1) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Marcin Mirończuk, Jarosław Protasiewicz | 7 | cites it | | 2025-04-10 | [Improving public transport through machine learning influence flow analysis (MIFA): Southern England bus case study](https://doi.org/10.1007/s12469-024-00387-2) | [Public Transport](https://scholar4ai.com/journals/public-transport.md) | Benjamin Lee, Wolfgang Garn, Masoud Fakhimi et al. | 1 | 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). C50 (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-c50 ```bibtex @misc{scholar4ai-code-cran-c50, author = {{Scholar4AI}}, title = {C50 (R package): papers linking to it}, year = {2026}, howpublished = {\url{https://scholar4ai.com/code/cran-c50}}, 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/).