randomForest (R package): papers linking to it

CRAN · CRAN.R-project.org/package=randomForest

← Code and data repositories cited in research papers

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

9 papers in the tracked journals link to randomForest (R package) in their Crossref records, the first in 2017; most of them (3) in 2021.
By category: Information Systems (2), Statistics (2) and Economics (1); the journals with the most are Land Use Policy (1), Journal of Open Innovation Technology Market and Complexity (1) and Electronic Markets (1).

By year#

20172025

Papers linking to randomForest (R package) by publication year
Year	Papers
2017	1
2018	1
2019	1
2021	3
2022	1
2023	1
2025	1

Journals#

Journals whose papers link to randomForest (R package) most
Journal	Papers
Land Use Policy	1
Journal of Open Innovation Technology Market and Complexity	1
Electronic Markets	1
Business & Information Systems Engineering	1
Scientometrics	1

Papers linking to randomForest (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2017-07-27	Bayesian Additive Regression Trees using Bayesian model averaging	Statistics and Computing	Belinda Hernández, Adrian E. Raftery, Stephen R. Pennington et al.	67	cites it
2019-11-20	Distributional regression for demand forecasting in e-grocery	European Journal of Operational Research	Matthias Ulrich, Hermann Jahnke, Roland Langrock et al.	56	cites it
2018-03-17	Enhancing energy efficiency in the residential sector with smart meter data analytics	Electronic Markets	Konstantin Hopf, Mariya Sodenkamp, Thorsten Staake	47	cites it
2021-03-10	Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany	Business & Information Systems Engineering	Simon Wenninger, Christian Wiethe	47	cites it
2022-06-09	Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values	Land Use Policy	Juan Pablo Carranza, Mario Andrés Piumetto, Carlos María Lucca et al.	26	cites it
2023-01-09	Ten reasons why research collaborations succeed—a random forest approach	Scientometrics	Malte Hückstädt	16	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
2021-02-26	Interpreting the black box of supervised learning models: Visualizing the impacts of features on prediction	Applied Intelligence	Xiaohang Zhang, Yuan Wang, Zhengren Li	6	cites it
2025-10-29	Combining variable screening methods for model averaging in high-dimensional data analysis	Annals of the Institute of Statistical Mathematics	Zhihao Zhao, Yuhong Yang, Li Wen	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). randomForest (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-randomforest

@misc{scholar4ai-code-cran-randomforest,
  author = {{Scholar4AI}},
  title = {randomForest (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-randomforest}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).