---
title: "discSurv (R package): papers citing this CRAN package"
description: "5 papers in the tracked journals link to discSurv (R package) in their Crossref records, the first in 2017; most of them (1) in 2026."
url: https://scholar4ai.com/code/cran-discsurv
markdown_url: https://scholar4ai.com/code/cran-discsurv.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to discSurv (R package) in their Crossref records, the first in 2017; most of them (1) in 2026. (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)
---

# discSurv (R package): papers linking to it

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

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

## Key findings

- 5 papers in the tracked journals link to discSurv (R package) in their Crossref records, the first in 2017; most of them (1) in 2026.
- By category: Statistics (4) and AI (1); the journals with the most are Econometrics and Statistics (2), Lifetime Data Analysis (2) and Data Mining and Knowledge Discovery (1).
- 3 of the papers build on one another: a later paper cites an earlier one that also links discSurv (R package).

## By year

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

| Year | Papers |
|---|---:|
| 2017 | 1 |
| 2018 | 1 |
| 2019 | 1 |
| 2020 | 1 |
| 2026 | 1 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Econometrics and Statistics](https://scholar4ai.com/journals/econometrics-and-statistics.md) | 2 |
| [Lifetime Data Analysis](https://scholar4ai.com/journals/lifetime-data-analysis.md) | 2 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Discrimination measures for discrete time-to-event predictions](https://doi.org/10.1016/j.ecosta.2017.03.008) (Econometrics and Statistics, 2017)
  - [Tree-based modeling of time-varying coefficients in discrete time-to-event models](https://doi.org/10.1007/s10985-019-09489-7) (Lifetime Data Analysis, 2019) — cites the paper above
  - [Discrete-time survival forests with Hellinger distance decision trees](https://doi.org/10.1007/s10618-020-00682-z) (Data Mining and Knowledge Discovery, 2020) — cites the paper above

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2017-04-21 | [Discrimination measures for discrete time-to-event predictions](https://doi.org/10.1016/j.ecosta.2017.03.008) | [Econometrics and Statistics](https://scholar4ai.com/journals/econometrics-and-statistics.md) | Matthias C. Schmid, Gerhard Tutz, Thomas Welchowski | 17 | cites it |
| 2019-11-11 | [Tree-based modeling of time-varying coefficients in discrete time-to-event models](https://doi.org/10.1007/s10985-019-09489-7) | [Lifetime Data Analysis](https://scholar4ai.com/journals/lifetime-data-analysis.md) | Marie‐Therese Puth, Gerhard Tutz, Nils Heim et al. | 17 | cites it |
| 2020-03-14 | [Discrete-time survival forests with Hellinger distance decision trees](https://doi.org/10.1007/s10618-020-00682-z) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Matthias C. Schmid, Thomas Welchowski, Marvin N. Wright et al. | 11 | cites it |
| 2018-12-20 | [The effect of explanatory variables on income: A tool that allows a closer look at the differences in income](https://doi.org/10.1016/j.ecosta.2018.12.001) | [Econometrics and Statistics](https://scholar4ai.com/journals/econometrics-and-statistics.md) | Gerhard Tutz, Moritz Berger | 2 | cites it |
| 2026-05-10 | [Reduction techniques for survival analysis](https://doi.org/10.1007/s10985-026-09714-0) | [Lifetime Data Analysis](https://scholar4ai.com/journals/lifetime-data-analysis.md) | Johannes Piller, Léa Orsini, Simon Wiegrebe et al. | 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). discSurv (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-discsurv

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