---
title: "keras (R package): papers citing this CRAN package"
description: "17 papers in the tracked journals link to keras (R package) in their Crossref records, the first in 2019; most of them (4) in 2022."
url: https://scholar4ai.com/code/cran-keras
markdown_url: https://scholar4ai.com/code/cran-keras.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "17 papers in the tracked journals link to keras (R package) in their Crossref records, the first in 2019; most of them (4) in 2022. (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)
---

# keras (R package): papers linking to it

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

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

## Key findings

- 17 papers in the tracked journals link to keras (R package) in their Crossref records, the first in 2019; most of them (4) in 2022.
- By category: Finance (6), Statistics (5) and AI (4); the journals with the most are European Actuarial Journal (6), Neural Computing and Applications (3) and Business & Information Systems Engineering (1).
- 6 of the papers build on one another: a later paper cites an earlier one that also links keras (R package).

## By year

*Papers linking to keras (R package) by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2021 | 4 |
| 2022 | 4 |
| 2023 | 2 |
| 2024 | 3 |
| 2025 | 3 |

## Journals

*Journals whose papers link to keras (R package) most*

| Journal | Papers |
|---|---:|
| [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | 6 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 3 |
| [Business & Information Systems Engineering](https://scholar4ai.com/journals/business-information-systems-engineering.md) | 1 |
| [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 1 |
| [Journal of Statistical Theory and Practice](https://scholar4ai.com/journals/journal-of-statistical-theory-and-practice.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Efficient use of data for LSTM mortality forecasting](https://doi.org/10.1007/s13385-022-00307-3) (European Actuarial Journal, 2022)
  - [A neural network approach for the mortality analysis of multiple populations: a case study on data of the Italian population](https://doi.org/10.1007/s13385-024-00377-5) (European Actuarial Journal, 2024) — cites the paper above
- [Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks](https://doi.org/10.1007/s13385-022-00314-4) (European Actuarial Journal, 2022)
  - [Combined modelling of micro-level outstanding claim counts and individual claim frequencies in non-life insurance](https://doi.org/10.1007/s13385-024-00383-7) (European Actuarial Journal, 2024) — cites the paper above
- [High-cardinality categorical covariates in network regressions](https://doi.org/10.1007/s42081-024-00243-4) (Japanese Journal of Statistics and Data Science, 2024)
  - [The credibility transformer](https://doi.org/10.1007/s13385-025-00413-y) (European Actuarial Journal, 2025) — cites the paper above

## Papers

*Papers linking to keras (R package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-03-10 | [Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany](https://doi.org/10.1007/s12599-021-00691-2) | [Business & Information Systems Engineering](https://scholar4ai.com/journals/business-information-systems-engineering.md) | Simon Wenninger, Christian Wiethe | 47 | cites it |
| 2022-04-02 | [Efficient use of data for LSTM mortality forecasting](https://doi.org/10.1007/s13385-022-00307-3) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | Mathias Lindholm, Lina Palmborg | 31 | cites it |
| 2022-04-01 | [Predicting reactions to anomalies in stock movements using a feed-forward deep learning network](https://doi.org/10.1016/j.jjimei.2022.100071) | [International Journal of Information Management Data Insights](https://scholar4ai.com/journals/international-journal-of-information-management-data-insights.md) | Talal Alsulaiman | 21 | cites it |
| 2023-11-08 | [A multi-task network approach for calculating discrimination-free insurance prices](https://doi.org/10.1007/s13385-023-00367-z) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | Mathias Lindholm, Ronald Richman, Andreas Tsanakas et al. | 14 | cites it |
| 2021-01-29 | [Deep neural networks architecture driven by problem-specific information](https://doi.org/10.1007/s00521-021-05702-7) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Daniel Urda, Francisco J. Veredas, Javier González-Enrique et al. | 12 | cites it |
| 2022-05-12 | [Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks](https://doi.org/10.1007/s13385-022-00314-4) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | Axel Bücher, Alexander Rosenstock | 12 | cites it |
| 2024-03-06 | [A neural network approach for the mortality analysis of multiple populations: a case study on data of the Italian population](https://doi.org/10.1007/s13385-024-00377-5) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | Maximilian Euthum, Matthias A. Scherer, Francesco Ungolo | 9 | cites it |
| 2019-06-22 | [Recognising innovative companies by using a diversified stacked generalisation method for website classification](https://doi.org/10.1007/s10489-019-01509-1) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Marcin Mirończuk, Jarosław Protasiewicz | 7 | cites it |
| 2025-02-15 | [The credibility transformer](https://doi.org/10.1007/s13385-025-00413-y) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | Ronald Richman, Salvatore Scognamiglio, Mario V. Wüthrich | 7 | cites it |
| 2021-09-11 | [tofee-tree: automatic feature engineering framework for modeling trend-cycle in time series forecasting](https://doi.org/10.1007/s00521-021-06438-0) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Santhosh Kumar Selvam, Chandrasekharan Rajendran | 5 | cites it |
| 2024-02-25 | [High-cardinality categorical covariates in network regressions](https://doi.org/10.1007/s42081-024-00243-4) | [Japanese Journal of Statistics and Data Science](https://scholar4ai.com/journals/japanese-journal-of-statistics-and-data-science.md) | Ronald Richman, Mario V. Wüthrich | 5 | cites it |
| 2022-12-22 | [EmulART: Emulating radiative transfer—a pilot study on autoencoder-based dimensionality reduction for radiative transfer models](https://doi.org/10.1007/s00521-022-08071-x) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | João Rino-Silvestre, Santiago González-Gaitán, Marko Stalevski et al. | 4 | cites it |
| 2025-01-13 | [Time series forecasting via integrating a filtering method: an application to electricity consumption](https://doi.org/10.1007/s00180-024-01595-x) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Felipe Leite Coelho da Silva, Josiane da Silva Cordeiro, Kleyton da Costa et al. | 4 | cites it |
| 2021-05-25 | [On Ensembles, I-Optimality, and Active Learning](https://doi.org/10.1007/s42519-021-00200-4) | [Journal of Statistical Theory and Practice](https://scholar4ai.com/journals/journal-of-statistical-theory-and-practice.md) | William D. Heavlin | 2 | cites it |
| 2023-04-25 | [Learning CHARME models with neural networks](https://doi.org/10.1007/s00362-023-01442-z) | [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | José G. Gómez-García, Jalal M. Fadili, Christophe Chesneau | 0 | cites it |
| 2024-04-24 | [Combined modelling of micro-level outstanding claim counts and individual claim frequencies in non-life insurance](https://doi.org/10.1007/s13385-024-00383-7) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | Axel Bücher, Alexander Rosenstock | 0 | cites it |
| 2025-07-20 | [DeepJAM: Joint alignment of multivariate quasi-periodic functional data using deep learning](https://doi.org/10.1007/s11222-025-10678-8) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Vi Thanh Pham, Jonas Bille Nielsen, Klaus Fuglsang Kofoed 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). keras (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-keras

```bibtex
@misc{scholar4ai-code-cran-keras,
  author = {{Scholar4AI}},
  title = {keras (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-keras}},
  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/).