---
title: "nleqslv (R package): papers citing this CRAN package"
description: "3 papers in the tracked journals link to nleqslv (R package) in their Crossref records, the first in 2017; most of them (2) in 2023."
url: https://scholar4ai.com/code/cran-nleqslv
markdown_url: https://scholar4ai.com/code/cran-nleqslv.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 nleqslv (R package) in their Crossref records, the first in 2017; most of them (2) in 2023. (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)
---

# nleqslv (R package): papers linking to it

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

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

## Key findings

- 3 papers in the tracked journals link to nleqslv (R package) in their Crossref records, the first in 2017; most of them (2) in 2023.
- By category: Statistics (3); the journals with the most are Statistics and Computing (1), Statistical Papers (1) and Spatial Statistics (1).

## By year

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

| Year | Papers |
|---|---:|
| 2017 | 1 |
| 2023 | 2 |

## Journals

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

| Journal | Papers |
|---|---:|
| [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 1 |
| [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | 1 |
| [Spatial Statistics](https://scholar4ai.com/journals/spatial-statistics.md) | 1 |

## Papers

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

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-03-25 | [Optimal subsampling design for polynomial regression in one covariate](https://doi.org/10.1007/s00362-023-01425-0) | [Statistical Papers](https://scholar4ai.com/journals/statistical-papers.md) | Torsten Reuter, Rainer Schwabe | 9 | cites it |
| 2023-09-11 | [Extreme value modeling with errors-in-variables in detection and attribution of changes in climate extremes](https://doi.org/10.1007/s11222-023-10290-8) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Yuen Tsz Abby Lau, Tianying Wang, Jun Yan et al. | 2 | cites it |
| 2017-03-23 | [Efficiently estimating some common geostatistical models by ‘energy–variance matching’ or its randomized ‘conditional–mean’ versions](https://doi.org/10.1016/j.spasta.2017.01.001) | [Spatial Statistics](https://scholar4ai.com/journals/spatial-statistics.md) | Didier Girard | 1 | 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). nleqslv (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-nleqslv

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