glmnet (R package): papers linking to it

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

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to glmnet (R package) (https://CRAN.R-project.org/package=glmnet), 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 glmnet (R package) in their Crossref records, the first in 2017; most of them (5) in 2019.
By category: Operations Research (5), AI (4) and Statistics (3); the journals with the most are Transportation (3), Statistics and Computing (2) and Data Mining and Knowledge Discovery (2).
2 of the papers build on one another: a later paper cites an earlier one that also links glmnet (R package).

By year#

20172026

Papers linking to glmnet (R package) by publication year
Year	Papers
2017	1
2018	2
2019	5
2020	3
2022	1
2023	1
2025	1
2026	1

Journals#

Journals whose papers link to glmnet (R package) most
Journal	Papers
Transportation	3
Statistics and Computing	2
Data Mining and Knowledge Discovery	2
Journal of Banking & Finance	1
International Journal of Forecasting	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method Transportation, 2020
- Integrated multiresolution framework for spatialized population synthesis Transportation, 2022 — cites the paper above

Papers linking to glmnet (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
2019-05-09	Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques	Optimization Letters	Sun Hye Kim, Fani Boukouvala	119	cites it
2018-06-06	Demand forecasting with user-generated online information	International Journal of Forecasting	Oliver Schaer, Nikolaos Kourentzes, Robert A. Fildes	71	cites it
2019-08-02	Sentiment spillover effects for US and European companies	Journal of Banking & Finance	Francesco Audrino, Anastasija Tetereva	67	cites it
2019-12-04	Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows	Transportation	Crispin Cooper, Ian Harvey, Scott Orford et al.	54	cites it
2020-02-18	Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method	Transportation	Nicholas Fournier, Eleni Christofa, Aditya Prakash et al.	26	cites it
2018-03-20	A Bayesian approach to forecasting daily air-pollutant levels	Knowledge and Information Systems	Jana Faganeli Pucer, Gregor Pirš, Erik Štrumbelj	18	cites it
2020-03-14	Discrete-time survival forests with Hellinger distance decision trees	Data Mining and Knowledge Discovery	Matthias C. Schmid, Thomas Welchowski, Marvin N. Wright et al.	11	cites it
2022-11-23	Integrated multiresolution framework for spatialized population synthesis	Transportation	Mohamed Khachman, Catherine Morency, Francesco Ciari	9	cites it
2017-06-21	Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter	Statistics and Computing	Hassan Pazira, Luigi Augugliaro, Ernst C. Wit	7	cites it
2019-01-08	Where will the next ski injury occur? A system for visual and predictive analytics of ski injuries	Operational Research	Sandro Radovanović, Boris Delibašić, Milija Suknović et al.	7	cites it
2023-04-16	Robust and sparse multinomial regression in high dimensions	Data Mining and Knowledge Discovery	Fatma Sevinç Kurnaz, Peter Filzmoser A	6	cites it
2019-06-24	MCEN: a method of simultaneous variable selection and clustering for high-dimensional multinomial regression	Statistics and Computing	Sheng Ren, Emily Lei Kang, Jason L. Lu	2	cites it
2025-11-10	Individualized Treatment Rules Based on Cost-Effectiveness Criteria in Microsimulations	PharmacoEconomics	Niklaus Meier, Ana Cecilia Quiroga Gutiérrez, Mark Pletscher et al.	1	cites it
2026-02-25	A framework to efficiently smooth L1 penalties for linear regression	Computational Statistics	Georg Hahn, Sharon M. Lutz, Nilanjana Laha 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). glmnet (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-glmnet

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

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