BTYDplus (R package): papers linking to it

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

← Code and data repositories cited in research papers

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

4 papers in the tracked journals link to BTYDplus (R package) in their Crossref records, the first in 2021; most of them (2) in 2022.
By category: Management (2), Marketing (1) and AI (1); the journals with the most are Journal of Business Economics (2), International Journal of Research in Marketing (1) and Expert Systems with Applications (1).
2 of the papers build on one another: a later paper cites an earlier one that also links BTYDplus (R package).

By year#

20212025

Papers linking to BTYDplus (R package) by publication year
Year	Papers
2021	1
2022	2
2025	1

Journals#

Journals whose papers link to BTYDplus (R package) most
Journal	Papers
Journal of Business Economics	2
International Journal of Research in Marketing	1
Expert Systems with Applications	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.

Customer base analysis with recurrent neural networks International Journal of Research in Marketing, 2022
- A generalised comparison of Pareto/NBD based forecasts using MCMC, maximum likelihood, and heuristics Journal of Business Economics, 2025 — cites the paper above

Papers linking to BTYDplus (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2022-03-11	Customer base analysis with recurrent neural networks	International Journal of Research in Marketing	Jan Valendin, Thomas Reutterer, Michael Platzer et al.	28	cites it
2022-04-28	Instant customer base analysis in the financial services sector	Expert Systems with Applications	Takhun Kim, Dongyeon Kim, Yongkil Ahn	6	cites it
2021-08-29	Worth the effort? Comparison of different MCMC algorithms for estimating the Pareto/NBD model	Journal of Business Economics	Lydia Simon, Jost Adler	5	cites it
2025-08-28	A generalised comparison of Pareto/NBD based forecasts using MCMC, maximum likelihood, and heuristics	Journal of Business Economics	Lydia Simon	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). BTYDplus (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-btydplus

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

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