---
title: "refund (R package): papers citing this CRAN package"
description: "7 papers in the tracked journals link to refund (R package) in their Crossref records, the first in 2017; most of them (2) in 2017."
url: https://scholar4ai.com/code/cran-refund
markdown_url: https://scholar4ai.com/code/cran-refund.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "7 papers in the tracked journals link to refund (R package) in their Crossref records, the first in 2017; most of them (2) in 2017. (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)
---

# refund (R package): papers linking to it

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

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

## Key findings

- 7 papers in the tracked journals link to refund (R package) in their Crossref records, the first in 2017; most of them (2) in 2017.
- By category: Statistics (7); the journals with the most are Statistics and Computing (4), Computational Statistics (1) and Computational Statistics & Data Analysis (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links refund (R package).

## By year

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

| Year | Papers |
|---|---:|
| 2017 | 2 |
| 2021 | 1 |
| 2022 | 1 |
| 2023 | 1 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 4 |
| [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 1 |
| [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | 1 |
| [Lifetime Data Analysis](https://scholar4ai.com/journals/lifetime-data-analysis.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Fast covariance estimation for sparse functional data](https://doi.org/10.1007/s11222-017-9744-8) (Statistics and Computing, 2017)
  - [Fast symmetric additive covariance smoothing](https://doi.org/10.1016/j.csda.2017.11.002) (Computational Statistics & Data Analysis, 2017) — cites the paper above

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2017-04-11 | [Fast covariance estimation for sparse functional data](https://doi.org/10.1007/s11222-017-9744-8) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Luo Xiao, Cai Li, William Checkley et al. | 74 | cites it |
| 2017-11-21 | [Fast symmetric additive covariance smoothing](https://doi.org/10.1016/j.csda.2017.11.002) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Jona Cederbaum, Fabian Scheipl, Sonja Greven | 11 | cites it |
| 2021-01-27 | [Bayesian wavelet-packet historical functional linear models](https://doi.org/10.1007/s11222-020-09981-3) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Mark J. Meyer, Elizabeth J. Malloy, Brent A. Coull | 6 | cites it |
| 2022-04-23 | [Selecting the derivative of a functional covariate in scalar-on-function regression](https://doi.org/10.1007/s11222-022-10091-5) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Giles Hooker, Han Lin Shang | 5 | cites it |
| 2026-02-23 | [Robust functional Cox regression model](https://doi.org/10.1007/s10985-026-09694-1) | [Lifetime Data Analysis](https://scholar4ai.com/journals/lifetime-data-analysis.md) | Gizel Bakıcıerler, Ufuk Beyaztaş | 2 | cites it |
| 2023-05-22 | [Tree-based boosting with functional data](https://doi.org/10.1007/s00180-023-01364-2) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Xiaomeng Ju, Matías Salibián‐Barrera | 1 | cites it |
| 2025-12-05 | [Penalized spatial function-on-function regression](https://doi.org/10.1007/s11222-025-10792-7) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Ufuk Beyaztaş, Han Lin Shang, Gizel Bakicierler Sezer | 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). refund (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-refund

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