--- title: "ks (R package): papers citing this CRAN package" description: "12 papers in the tracked journals link to ks (R package) in their Crossref records, the first in 2016; most of them (2) in 2025." url: https://scholar4ai.com/code/cran-ks markdown_url: https://scholar4ai.com/code/cran-ks.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "12 papers in the tracked journals link to ks (R package) in their Crossref records, the first in 2016; most of them (2) in 2025. (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) --- # ks (R package): papers linking to it Research papers in the 480 tracked journals that link to ks (R package) (https://CRAN.R-project.org/package=ks), 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=ks All repositories: https://scholar4ai.com/code.md ## Key findings - 12 papers in the tracked journals link to ks (R package) in their Crossref records, the first in 2016; most of them (2) in 2025. - By category: Statistics (8), AI (2) and Operations Research (1); the journals with the most are Computational Statistics & Data Analysis (3), Statistics and Computing (2) and Annals of Operations Research (1). - 2 of the papers build on one another: a later paper cites an earlier one that also links ks (R package). ## By year *Papers linking to ks (R package) by publication year* | Year | Papers | |---|---:| | 2016 | 1 | | 2017 | 1 | | 2018 | 2 | | 2019 | 2 | | 2020 | 1 | | 2021 | 1 | | 2023 | 1 | | 2025 | 2 | | 2026 | 1 | ## Journals *Journals whose papers link to ks (R package) most* | Journal | Papers | |---|---:| | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | 3 | | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 2 | | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | 1 | | [Journal of Multivariate Analysis](https://scholar4ai.com/journals/journal-of-multivariate-analysis.md) | 1 | | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | 1 | ## Family tree Each paper sits under the earlier paper it builds on. - [D-vine copula based quantile regression](https://doi.org/10.1016/j.csda.2016.12.009) (Computational Statistics & Data Analysis, 2016) - [The COVID-19 pandemic and speculation in energy, precious metals, and agricultural futures](https://doi.org/10.1016/j.jbef.2021.100498) (Journal of Behavioral and Experimental Finance, 2021) — cites the paper above ## Papers *Papers linking to ks (R package), most cited first* | Published | Title | Journal | Authors | Citations | Link | |---|---|---|---|---:|---| | 2016-12-28 | [D-vine copula based quantile regression](https://doi.org/10.1016/j.csda.2016.12.009) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Daniel Kraus, Claudia Czado | 183 | cites it | | 2019-11-21 | [On normalization and algorithm selection for unsupervised outlier detection](https://doi.org/10.1007/s10618-019-00661-z) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Sevvandi Kandanaarachchi, Mario Andrés Muñoz, Rob J. Hyndman et al. | 62 | cites it | | 2021-03-23 | [The COVID-19 pandemic and speculation in energy, precious metals, and agricultural futures](https://doi.org/10.1016/j.jbef.2021.100498) | [Journal of Behavioral and Experimental Finance](https://scholar4ai.com/journals/journal-of-behavioral-and-experimental-finance.md) | Imtiaz Mohammad Sifat, Abdul Ghafoor, Abdollah Ah Mand | 57 | cites it | | 2017-03-09 | [A nonparametric test for the comparison of first-order structures of spatial point processes](https://doi.org/10.1016/j.spasta.2017.02.007) | [Spatial Statistics](https://scholar4ai.com/journals/spatial-statistics.md) | Isabel Fuentes‐Santos, Wenceslao González–Manteiga, Jorge Mateu | 14 | cites it | | 2019-10-22 | [Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates](https://doi.org/10.1016/j.csda.2019.106875) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | M. I. Borrajo, Wenceslao González–Manteiga, María Dolores Martínez‐Miranda | 10 | cites it | | 2018-09-05 | [Modeling uncertainty of expert elicitation for use in risk-based optimization](https://doi.org/10.1007/s10479-018-3011-z) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Michael D. Teter, Johannes Ø. Røyset, Alexandra M. Newman | 6 | cites it | | 2018-10-08 | [Robust two-sample test of high-dimensional mean vectors under dependence](https://doi.org/10.1016/j.jmva.2018.09.013) | [Journal of Multivariate Analysis](https://scholar4ai.com/journals/journal-of-multivariate-analysis.md) | Wei Wang, Nan Lin, Xiang Tang | 6 | cites it | | 2020-11-30 | [Kernel machines for current status data](https://doi.org/10.1007/s10994-020-05930-3) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Yael Travis-Lumer, Yair Goldberg | 6 | cites it | | 2025-09-04 | [Kernel density estimation with a Markov chain Monte Carlo sample](https://doi.org/10.1016/j.csda.2025.108271) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Hang Joon Kim, Steven N. MacEachern, Young Min Kim et al. | 4 | cites it | | 2023-01-20 | [Moment-based density estimation of confidential micro-data: a computational statistics approach](https://doi.org/10.1007/s11222-022-10203-1) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Bradley Wakefield, Yan‐Xia Lin, Rathindra Sarathy et al. | 2 | cites it | | 2025-09-12 | [Robust mean-shift clustering based on impartial trimming](https://doi.org/10.1007/s11222-025-10718-3) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Luca Greco, Giovanna Menardi | 0 | cites it | | 2026-07-07 | [Interpretable contour level selection for heat maps for gridded data](https://doi.org/10.1007/s00180-026-01767-x) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Tarn Duong | 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). ks (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-ks ```bibtex @misc{scholar4ai-code-cran-ks, author = {{Scholar4AI}}, title = {ks (R package): papers linking to it}, year = {2026}, howpublished = {\url{https://scholar4ai.com/code/cran-ks}}, 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/).