COMPoissonReg (R package): papers linking to it

CRAN · CRAN.R-project.org/package=COMPoissonReg

← Code and data repositories cited in research papers

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

Key findings#

3 papers in the tracked journals link to COMPoissonReg (R package) in their Crossref records, the first in 2017; most of them (1) in 2024.
By category: Statistics (3); the journals with the most are Statistics and Computing (2) and Computational Statistics (1).
3 of the papers build on one another: a later paper cites an earlier one that also links COMPoissonReg (R package).

By year#

20172024

Papers linking to COMPoissonReg (R package) by publication year
Year	Papers
2017	1
2023	1
2024	1

Journals#

Journals whose papers link to COMPoissonReg (R package) most
Journal	Papers
Statistics and Computing	2
Computational Statistics	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Efficient Bayesian inference for COM-Poisson regression models Statistics and Computing, 2017
- A fast look-up method for Bayesian mean-parameterised Conway–Maxwell–Poisson regression models Statistics and Computing, 2023 — cites the paper above
- A simple algorithm for computing the probabilities of count models based on pure birth processes Computational Statistics, 2024 — cites the paper above

Papers linking to COMPoissonReg (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2017-04-24	Efficient Bayesian inference for COM-Poisson regression models	Statistics and Computing	Charalampos Chanialidis, Ludger Evers A, Tereza Neocleous et al.	37	cites it
2023-05-18	A fast look-up method for Bayesian mean-parameterised Conway–Maxwell–Poisson regression models	Statistics and Computing	Pete Philipson, Alan Huang	8	cites it
2024-04-10	A simple algorithm for computing the probabilities of count models based on pure birth processes	Computational Statistics	Mongkol Hunkrajok, Wanrudee Skulpakdee	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). COMPoissonReg (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-compoissonreg

@misc{scholar4ai-code-cran-compoissonreg,
  author = {{Scholar4AI}},
  title = {COMPoissonReg (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-compoissonreg}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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