gbm (R package): papers linking to it

← Code and data repositories cited in research papers

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

15 papers in the tracked journals link to gbm (R package) in their Crossref records, the first in 2017; most of them (3) in 2018.
By category: Economics (4), Statistics (3) and AI (3); the journals with the most are Computational Statistics (2), European Actuarial Journal (1) and Land Use Policy (1).

By year#

20172025

Papers linking to gbm (R package) by publication year
Year	Papers
2017	2
2018	3
2019	1
2021	2
2022	2
2023	1
2024	2
2025	2

Journals#

Journals whose papers link to gbm (R package) most
Journal	Papers
Computational Statistics	2
European Actuarial Journal	1
Land Use Policy	1
Electronic Markets	1
Neural Networks	1

Papers linking to gbm (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2018-12-21	An extensive experimental survey of regression methods	Neural Networks	Manuel Fernández-Delgado, Manisha Sanjay Sirsat, Eva Cernadas et al.	380	cites it
2017-06-14	Predicting recessions with boosted regression trees	International Journal of Forecasting	Jörg Döpke, Ulrich Fritsche, Christian Pierdzioch	117	cites it
2019-06-08	Predicting missing values: a comparative study on non-parametric approaches for imputation	Computational Statistics	Burim Ramosaj, Markus Pauly	47	cites it
2022-09-14	How, and For Whom, Does Higher Education Increase Voting?	Research in Higher Education	Caitlin E. Ahearn, Jennie E. Brand, Xiang Zhou	36	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
2021-03-03	Bayesian additive regression trees with model trees	Statistics and Computing	Estevão B. Prado, Rafael de Andrade Moral, Andrew C. Parnell	30	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
2023-12-06	Ineffective built environment interventions: How to reduce driving in American suburbs?	Transportation Research Part A Policy and Practice	Tao Tao, Xinyu Cao	22	cites it
2018-12-19	Improving the drug discovery process by using multiple classifier systems	Expert Systems with Applications	David Ruano-Ordás, Iryna Yevseyeva, Vítor Basto-Fernandes et al.	21	cites it
2017-09-13	A survival ensemble of extreme learning machine	Applied Intelligence	Hong Wang, Jianxin Wang, Lifeng Zhou	14	cites it
2018-11-15	Power comparison for propensity score methods	Computational Statistics	Byeong Yeob Choi, Chen‐Pin Wang, Joel Edmund Michalek et al.	14	cites it
2021-05-17	GEM: A short “Growth-vs-Environment” Module for survey research	Ecological Economics	Ivan V. Savin, Stefan Drews, Jeroen C.J.M. van den Bergh	9	cites it
2025-04-16	A machine learning approach to assessing multidimensional poverty and targeting assistance among forcibly displaced populations	World Development	Angela Christine Lyons, Alejandro Montoya Castano, Joséphine Kass-Hanna et al.	8	cites it
2024-05-27	On duration effects in non-life insurance pricing	European Actuarial Journal	Mathias Lindholm, Taariq Nazar	5	cites it
2025-07-28	Machine learning-driven insights for business innovation: Analyzing financial performance in Vietnam’s emerging economy 2010–2024	Journal of Open Innovation Technology Market and Complexity	Phạm Trung Hiếu, Thi Hang, Doan Huynh Thu Hoai et al.	2	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). gbm (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-gbm

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

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