---
title: "expectreg (R package): papers citing this CRAN package"
description: "6 papers in the tracked journals link to expectreg (R package) in their Crossref records, the first in 2016; most of them (1) in 2026."
url: https://scholar4ai.com/code/cran-expectreg
markdown_url: https://scholar4ai.com/code/cran-expectreg.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to expectreg (R package) in their Crossref records, the first in 2016; most of them (1) in 2026. (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)
---

# expectreg (R package): papers linking to it

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

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

## Key findings

- 6 papers in the tracked journals link to expectreg (R package) in their Crossref records, the first in 2016; most of them (1) in 2026.
- By category: Statistics (4), Finance (1) and AI (1); the journals with the most are Computational Statistics & Data Analysis (2), European Actuarial Journal (1) and Statistics and Computing (1).
- 4 of the papers build on one another: a later paper cites an earlier one that also links expectreg (R package).

## By year

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

| Year | Papers |
|---|---:|
| 2016 | 1 |
| 2017 | 1 |
| 2020 | 1 |
| 2024 | 1 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | 2 |
| [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | 1 |
| [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 1 |
| [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 1 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [An SVM-like approach for expectile regression](https://doi.org/10.1016/j.csda.2016.11.010) (Computational Statistics & Data Analysis, 2016)
  - [KLERC: kernel Lagrangian expectile regression calculator](https://doi.org/10.1007/s00180-020-01003-0) (Computational Statistics, 2020) — cites the paper above
- [An expectile computation cookbook](https://doi.org/10.1007/s11222-024-10403-x) (Statistics and Computing, 2024)
  - [Expectiles as basis risk-optimal payment schemes in parametric insurance](https://doi.org/10.1007/s13385-026-00447-w) (European Actuarial Journal, 2026) — cites the paper above

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2016-12-16 | [An SVM-like approach for expectile regression](https://doi.org/10.1016/j.csda.2016.11.010) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Muhammad Farooq, Ingo Steinwart | 43 | cites it |
| 2017-07-29 | [A continuous threshold expectile model](https://doi.org/10.1016/j.csda.2017.07.005) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Feipeng Zhang, Qunhua Li | 37 | cites it |
| 2024-03-23 | [An expectile computation cookbook](https://doi.org/10.1007/s11222-024-10403-x) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Abdelaati Daouia, Gilles Stupfler, Antoine Usseglio‐Carleve | 8 | cites it |
| 2025-03-05 | [Deep Huber quantile regression networks](https://doi.org/10.1016/j.neunet.2025.107364) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Hristos Tyralis, Georgia Papacharalampous, Nilay Doğulu et al. | 7 | cites it |
| 2026-02-26 | [Expectiles as basis risk-optimal payment schemes in parametric insurance](https://doi.org/10.1007/s13385-026-00447-w) | [European Actuarial Journal](https://scholar4ai.com/journals/european-actuarial-journal.md) | Martin Maier, Matthias A. Scherer | 2 | cites it |
| 2020-06-25 | [KLERC: kernel Lagrangian expectile regression calculator](https://doi.org/10.1007/s00180-020-01003-0) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Songfeng Zheng | 1 | 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). expectreg (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-expectreg

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