xgboost (R package): papers linking to it

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

← Code and data repositories cited in research papers

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

27 papers in the tracked journals link to xgboost (R package) in their Crossref records, the first in 2018; most of them (11) in 2024.
By category: AI (14), Economics (3) and Operations Research (3); the journals with the most are IEEE Transactions on Neural Networks and Learning Systems (3), IEEE Transactions on Knowledge and Data Engineering (3) and Neural Computing and Applications (2).

By year#

20182026

Papers linking to xgboost (R package) by publication year
Year	Papers
2018	1
2019	1
2020	2
2021	3
2022	1
2023	3
2024	11
2025	3
2026	2

Journals#

Journals whose papers link to xgboost (R package) most
Journal	Papers
IEEE Transactions on Neural Networks and Learning Systems	3
IEEE Transactions on Knowledge and Data Engineering	3
Neural Computing and Applications	2
Health Care Management Science	2
Expert Systems with Applications	2

Papers linking to xgboost (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2020-07-06	Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms	Neural Computing and Applications	Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis	177	cites it
2018-08-07	Incorporating domain knowledge in machine learning for soccer outcome prediction	Machine Learning	Daniel Berrar, Philippe Lopes, Werner Dubitzky	91	cites it
2021-01-06	Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting	Computational Economics	Matthew Smith, Francisco Álvarez	52	cites it
2023-09-28	An integrated approach of ensemble learning methods for stock index prediction using investor sentiments	Expert Systems with Applications	Shangkun Deng, Yingke Zhu, Yiting Yu et al.	52	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
2023-03-13	Predicting drug shortages using pharmacy data and machine learning	Health Care Management Science	Raman Pall, Yvan Gauthier, Sofia Auer et al.	44	cites it
2024-02-14	Meta-MolNet: A Cross-Domain Benchmark for Few Examples Drug Discovery	IEEE Transactions on Neural Networks and Learning Systems	Qiujie Lv, Guanxing Chen, Ziduo Yang et al.	39	cites it
2024-01-13	A framework to improve churn prediction performance in retail banking	Financial Innovation	João B. G. Brito, Guilherme Brandelli Bucco, Rodrigo Heldt et al.	35	cites it
2020-07-06	Machine learning for total cloud cover prediction	Neural Computing and Applications	Ágnes Baran, Sebastian Lerch, Mehrez El Ayari et al.	34	cites it
2024-01-23	Consumers’ ambiguous perceptions of advertising disclosures in influencer marketing: Disentangling the effects on current and future social media engagement	Electronic Markets	Adrian Waltenrath	31	cites it
2023-01-30	Adaptive solution prediction for combinatorial optimization	European Journal of Operational Research	Yunzhuang Shen, Yuan Sun, Xiaodong Li et al.	13	cites it
2025-08-04	From point to probabilistic gradient boosting for claim frequency and severity prediction	European Actuarial Journal	Dominik Chevalier, Marie‐Pier Côté	13	cites it
2024-07-03	Prediction-led prescription: Optimal Decision-Making in times of turbulence and business performance improvement	Journal of Business Research	Andreas Schäfers, Vasileios Bougioukos, Georgios Karamatzanis et al.	11	cites it
2024-07-02	StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification	IEEE Transactions on Artificial Intelligence	Tasmin Karim, Md. Shazzad Hossain Shaon, Md. Mamun Ali et al.	10	cites it
2024-08-15	A nowcasting model of industrial production using alternative data and machine learning approaches	Japan and the World Economy	Kakuho Furukawa, Ryohei Hisano, Yukio Minoura et al.	10	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
2024-01-19	Heterogeneity of national accounting systems, world-class universities and financial resources: What are the links?	Journal of Informetrics	Alessandro Avenali, Cinzia C. Daraio, Simone Di Leo et al.	8	cites it
2024-04-01	Bootstrapping on Continuous-Time Dynamic Graphs for Crowd Flow Modeling	IEEE Transactions on Knowledge and Data Engineering	Yi Xu, Liangzhe Han, Leilei Sun et al.	8	cites it
2019-06-22	Recognising innovative companies by using a diversified stacked generalisation method for website classification	Applied Intelligence	Marcin Mirończuk, Jarosław Protasiewicz	7	cites it
2022-07-27	Aiding the prescriber: developing a machine learning approach to personalized risk modeling for chronic opioid therapy amongst US Army soldiers	Health Care Management Science	Margrét V. Bjarnadóttir, David Anderson, Ritu Agarwal et al.	7	cites it
2024-01-09	Machine learning approaches to identify lithium concentration in petroleum produced waters	Mineral Economics	Emil D. Attanasi, Timothy C. Coburn, Philip A. Freeman	6	cites it
2024-01-31	Deep Ensemble Transformers for Dimensionality Reduction	IEEE Transactions on Neural Networks and Learning Systems	Maria Nareklishvili, Marius Geitle	6	cites it
2024-11-13	Manifoldron: Direct Space Partition via Manifold Discovery	IEEE Transactions on Neural Networks and Learning Systems	Dayang Wang, Fenglei Fan, Bojian Hou et al.	6	cites it
2025-08-22	Spindle thermal error modeling with linear and nonlinear hybrid tandem metamodel based on statistical and machine learning approaches	Expert Systems with Applications	Menghua Li, Mingchang Chih, Chia-Fu Kou et al.	2	cites it
2025-01-24	HPST-GT: Full-Link Delivery Time Estimation Via Heterogeneous Periodic Spatial-Temporal Graph Transformer	IEEE Transactions on Knowledge and Data Engineering	Shuai Wang, Hai Wang, Li Lin et al.	1	cites it
2026-05-12	Semi-Supervised Adaptive Fast XGBoost for Multiclass Stream Classification	IEEE Transactions on Knowledge and Data Engineering	Deividy Amorim Policarpo, Yuji Yamada Correa, Fabiano Baldo	0	cites it
2026-07-27	Solving Imbalanced Problems With Multilayer Ensemble Balanced Fuzzy Inference Broad Network	IEEE Transactions on Fuzzy Systems	Wuxing Chen, Zhiwen Yu, Kaixiang Yang et al.	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). xgboost (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-xgboost

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

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