---
title: "RcppHungarian (R package): papers citing this CRAN package"
description: "3 papers in the tracked journals link to RcppHungarian (R package) in their Crossref records, the first in 2023; most of them (2) in 2025."
url: https://scholar4ai.com/code/cran-rcpphungarian
markdown_url: https://scholar4ai.com/code/cran-rcpphungarian.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to RcppHungarian (R package) in their Crossref records, the first in 2023; most of them (2) in 2025. (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)
---

# RcppHungarian (R package): papers linking to it

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

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

## Key findings

- 3 papers in the tracked journals link to RcppHungarian (R package) in their Crossref records, the first in 2023; most of them (2) in 2025.
- By category: Statistics (2) and Operations Research (1); the journals with the most are Annals of Operations Research (1), Statistical Papers (1) and Computational Statistics (1).
- 3 of the papers build on one another: a later paper cites an earlier one that also links RcppHungarian (R package).

## By year

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

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

## Journals

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

| Journal | Papers |
|---|---:|
| [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | 1 |
| [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | 1 |
| [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Variational Bayes estimation of hierarchical Dirichlet-multinomial mixtures for text clustering](https://doi.org/10.1007/s00180-023-01350-8) (Computational Statistics, 2023)
  - [Stochastic variational inference for clustering short text data with finite mixtures of Dirichlet-Multinomial distributions](https://doi.org/10.1007/s00362-025-01702-0) (Statistical Papers, 2025) — cites the paper above
    - [Hierarchical mixtures of Unigram models for short text clustering: the role of Beta-Liouville priors](https://doi.org/10.1007/s10479-025-06693-7) (Annals of Operations Research, 2025) — cites the paper above

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-05-15 | [Variational Bayes estimation of hierarchical Dirichlet-multinomial mixtures for text clustering](https://doi.org/10.1007/s00180-023-01350-8) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Massimo Bilancia, Michele Di Nanni, Fabio Manca et al. | 8 | cites it |
| 2025-04-25 | [Stochastic variational inference for clustering short text data with finite mixtures of Dirichlet-Multinomial distributions](https://doi.org/10.1007/s00362-025-01702-0) | [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | Massimo Bilancia, Andrea Nigri, Samuele Magro | 2 | cites it |
| 2025-06-24 | [Hierarchical mixtures of Unigram models for short text clustering: the role of Beta-Liouville priors](https://doi.org/10.1007/s10479-025-06693-7) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Massimo Bilancia, Samuele Magro | 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). RcppHungarian (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-rcpphungarian

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