---
title: "glmnet (R package): papers citing this CRAN package"
description: "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."
url: https://scholar4ai.com/code/cran-glmnet
markdown_url: https://scholar4ai.com/code/cran-glmnet.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 glmnet (R package) in their Crossref records, the first in 2017; most of them (5) in 2019. (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)
---

# glmnet (R package): papers linking to it

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.

- CRAN
- https://CRAN.R-project.org/package=glmnet

All repositories: https://scholar4ai.com/code.md

## 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

*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](https://scholar4ai.com/journals/transportation.md) | 3 |
| [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 2 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 2 |
| [Journal of Banking & Finance](https://scholar4ai.com/journals/journal-of-banking-finance.md) | 1 |
| [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method](https://doi.org/10.1007/s11116-020-10090-3) (Transportation, 2020)
  - [Integrated multiresolution framework for spatialized population synthesis](https://doi.org/10.1007/s11116-022-10358-w) (Transportation, 2022) — cites the paper above

## Papers

*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](https://doi.org/10.1007/s00521-020-05172-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 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](https://doi.org/10.1007/s11590-019-01428-7) | [Optimization Letters](https://scholar4ai.com/journals/optimization-letters.md) | Sun Hye Kim, Fani Boukouvala | 119 | cites it |
| 2018-06-06 | [Demand forecasting with user-generated online information](https://doi.org/10.1016/j.ijforecast.2018.03.005) | [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | Oliver Schaer, Nikolaos Kourentzes, Robert A. Fildes | 71 | cites it |
| 2019-08-02 | [Sentiment spillover effects for US and European companies](https://doi.org/10.1016/j.jbankfin.2019.07.022) | [Journal of Banking & Finance](https://scholar4ai.com/journals/journal-of-banking-finance.md) | 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](https://doi.org/10.1007/s11116-019-10072-0) | [Transportation](https://scholar4ai.com/journals/transportation.md) | 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](https://doi.org/10.1007/s11116-020-10090-3) | [Transportation](https://scholar4ai.com/journals/transportation.md) | Nicholas Fournier, Eleni Christofa, Aditya Prakash et al. | 26 | cites it |
| 2018-03-20 | [A Bayesian approach to forecasting daily air-pollutant levels](https://doi.org/10.1007/s10115-018-1177-y) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Jana Faganeli Pucer, Gregor Pirš, Erik Štrumbelj | 18 | cites it |
| 2020-03-14 | [Discrete-time survival forests with Hellinger distance decision trees](https://doi.org/10.1007/s10618-020-00682-z) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Matthias C. Schmid, Thomas Welchowski, Marvin N. Wright et al. | 11 | cites it |
| 2022-11-23 | [Integrated multiresolution framework for spatialized population synthesis](https://doi.org/10.1007/s11116-022-10358-w) | [Transportation](https://scholar4ai.com/journals/transportation.md) | Mohamed Khachman, Catherine Morency, Francesco Ciari | 9 | cites it |
| 2017-06-21 | [Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter](https://doi.org/10.1007/s11222-017-9761-7) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 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](https://doi.org/10.1007/s12351-018-00449-x) | [Operational Research](https://scholar4ai.com/journals/operational-research.md) | Sandro Radovanović, Boris Delibašić, Milija Suknović et al. | 7 | cites it |
| 2023-04-16 | [Robust and sparse multinomial regression in high dimensions](https://doi.org/10.1007/s10618-023-00936-6) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 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](https://doi.org/10.1007/s11222-019-09880-2) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Sheng Ren, Emily Lei Kang, Jason L. Lu | 2 | cites it |
| 2025-11-10 | [Individualized Treatment Rules Based on Cost-Effectiveness Criteria in Microsimulations](https://doi.org/10.1007/s40273-025-01562-4) | [PharmacoEconomics](https://scholar4ai.com/journals/pharmacoeconomics.md) | 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](https://doi.org/10.1007/s00180-025-01710-6) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).