--- title: "gbm (R package): papers citing this CRAN package" description: "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." url: https://scholar4ai.com/code/cran-gbm markdown_url: https://scholar4ai.com/code/cran-gbm.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "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. (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) --- # gbm (R package): papers linking to it 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. - CRAN - https://CRAN.R-project.org/package=gbm All repositories: https://scholar4ai.com/code.md ## 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 *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](https://scholar4ai.com/journals/computational-statistics.md) | 2 | | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | 1 | | [Land Use Policy](https://scholar4ai.com/journals/land-use-policy.md) | 1 | | [Electronic Markets](https://scholar4ai.com/journals/electronic-markets.md) | 1 | | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 | ## Papers *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](https://doi.org/10.1016/j.neunet.2018.12.010) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Manuel Fernández-Delgado, Manisha Sanjay Sirsat, Eva Cernadas et al. | 380 | cites it | | 2017-06-14 | [Predicting recessions with boosted regression trees](https://doi.org/10.1016/j.ijforecast.2017.02.003) | [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | Jörg Döpke, Ulrich Fritsche, Christian Pierdzioch | 117 | 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 | | 2022-09-14 | [How, and For Whom, Does Higher Education Increase Voting?](https://doi.org/10.1007/s11162-022-09717-4) | [Research in Higher Education](https://scholar4ai.com/journals/research-in-higher-education.md) | 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](https://doi.org/10.1007/s12525-023-00679-8) | [Electronic Markets](https://scholar4ai.com/journals/electronic-markets.md) | Adrian Waltenrath | 31 | cites it | | 2021-03-03 | [Bayesian additive regression trees with model trees](https://doi.org/10.1007/s11222-021-09997-3) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 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](https://doi.org/10.1016/j.landusepol.2022.106211) | [Land Use Policy](https://scholar4ai.com/journals/land-use-policy.md) | 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?](https://doi.org/10.1016/j.tra.2023.103924) | [Transportation Research Part A Policy and Practice](https://scholar4ai.com/journals/transportation-research-part-a-policy-and-practice.md) | Tao Tao, Xinyu Cao | 22 | cites it | | 2018-12-19 | [Improving the drug discovery process by using multiple classifier systems](https://doi.org/10.1016/j.eswa.2018.12.032) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | David Ruano-Ordás, Iryna Yevseyeva, Vítor Basto-Fernandes et al. | 21 | cites it | | 2017-09-13 | [A survival ensemble of extreme learning machine](https://doi.org/10.1007/s10489-017-1063-4) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Hong Wang, Jianxin Wang, Lifeng Zhou | 14 | cites it | | 2018-11-15 | [Power comparison for propensity score methods](https://doi.org/10.1007/s00180-018-0852-5) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 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](https://doi.org/10.1016/j.ecolecon.2021.107092) | [Ecological Economics](https://scholar4ai.com/journals/ecological-economics.md) | 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](https://doi.org/10.1016/j.worlddev.2025.107013) | [World Development](https://scholar4ai.com/journals/world-development.md) | 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](https://doi.org/10.1007/s13385-024-00385-5) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | 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](https://doi.org/10.1016/j.joitmc.2025.100605) | [Journal of Open Innovation Technology Market and Complexity](https://scholar4ai.com/journals/journal-of-open-innovation-technology-market-and-complexity.md) | 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 ```bibtex @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} } ``` License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).