---
title: "BTYDplus (R package): papers citing this CRAN package"
description: "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."
url: https://scholar4ai.com/code/cran-btydplus
markdown_url: https://scholar4ai.com/code/cran-btydplus.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 BTYDplus (R package) in their Crossref records, the first in 2021; most of them (2) in 2022. (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)
---

# BTYDplus (R package): papers linking to it

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.

- CRAN
- https://CRAN.R-project.org/package=BTYDplus

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

## 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

*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](https://scholar4ai.com/journals/journal-of-business-economics.md) | 2 |
| [International Journal of Research in Marketing](https://scholar4ai.com/journals/international-journal-of-research-in-marketing.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Customer base analysis with recurrent neural networks](https://doi.org/10.1016/j.ijresmar.2022.02.007) (International Journal of Research in Marketing, 2022)
  - [A generalised comparison of Pareto/NBD based forecasts using MCMC, maximum likelihood, and heuristics](https://doi.org/10.1007/s11573-025-01237-8) (Journal of Business Economics, 2025) — cites the paper above

## Papers

*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](https://doi.org/10.1016/j.ijresmar.2022.02.007) | [International Journal of Research in Marketing](https://scholar4ai.com/journals/international-journal-of-research-in-marketing.md) | Jan Valendin, Thomas Reutterer, Michael Platzer et al. | 28 | cites it |
| 2022-04-28 | [Instant customer base analysis in the financial services sector](https://doi.org/10.1016/j.eswa.2022.117326) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 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](https://doi.org/10.1007/s11573-021-01057-6) | [Journal of Business Economics](https://scholar4ai.com/journals/journal-of-business-economics.md) | Lydia Simon, Jost Adler | 5 | cites it |
| 2025-08-28 | [A generalised comparison of Pareto/NBD based forecasts using MCMC, maximum likelihood, and heuristics](https://doi.org/10.1007/s11573-025-01237-8) | [Journal of Business Economics](https://scholar4ai.com/journals/journal-of-business-economics.md) | 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).