---
title: "maxLik (R package): papers citing this CRAN package"
description: "4 papers in the tracked journals link to maxLik (R package) in their Crossref records, the first in 2022; most of them (2) in 2023."
url: https://scholar4ai.com/code/cran-maxlik
markdown_url: https://scholar4ai.com/code/cran-maxlik.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to maxLik (R package) in their Crossref records, the first in 2022; most of them (2) in 2023. (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)
---

# maxLik (R package): papers linking to it

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

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

## Key findings

- 4 papers in the tracked journals link to maxLik (R package) in their Crossref records, the first in 2022; most of them (2) in 2023.
- By category: Operations Research (2) and Statistics (2); the journals with the most are Transportation Research Part A Policy and Practice (2) and Computational Statistics (2).
- 2 of the papers build on one another: a later paper cites an earlier one that also links maxLik (R package).

## By year

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

| Year | Papers |
|---|---:|
| 2022 | 1 |
| 2023 | 2 |
| 2025 | 1 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Transportation Research Part A Policy and Practice](https://scholar4ai.com/journals/transportation-research-part-a-policy-and-practice.md) | 2 |
| [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 2 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Public preferences for distribution in the context of transport investments](https://doi.org/10.1016/j.tra.2022.01.013) (Transportation Research Part A Policy and Practice, 2022)
  - [Do the distributional preferences of national infrastructure planners diverge from those of the public?](https://doi.org/10.1016/j.tra.2023.103621) (Transportation Research Part A Policy and Practice, 2023) — cites the paper above

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2022-02-03 | [Public preferences for distribution in the context of transport investments](https://doi.org/10.1016/j.tra.2022.01.013) | [Transportation Research Part A Policy and Practice](https://scholar4ai.com/journals/transportation-research-part-a-policy-and-practice.md) | Anders Bondemark, Henrik Andersson, Karin Brundell-Freij | 7 | cites it |
| 2023-03-01 | [Do the distributional preferences of national infrastructure planners diverge from those of the public?](https://doi.org/10.1016/j.tra.2023.103621) | [Transportation Research Part A Policy and Practice](https://scholar4ai.com/journals/transportation-research-part-a-policy-and-practice.md) | Anders Bondemark, Henrik Andersson, Karin Brundell-Freij | 2 | cites it |
| 2023-05-02 | [An expectation conditional maximization algorithm for the skew-normal based stochastic frontier model](https://doi.org/10.1007/s00180-023-01356-2) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Xiaonan Zhu, Zheng Mao Wei, Tonghui Wang et al. | 2 | cites it |
| 2025-05-28 | [Maximum likelihood estimation of parameters for double poisson regression: a simulation study](https://doi.org/10.1007/s00180-025-01636-z) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Sebastian Appelbaum, Thomas Ostermann, Uwe Konerding | 2 | 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). maxLik (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-maxlik

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