microbenchmark (R package): papers linking to it

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

← Code and data repositories cited in research papers

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

10 papers in the tracked journals link to microbenchmark (R package) in their Crossref records, the first in 2017; most of them (2) in 2025.
By category: Statistics (7), AI (2) and Economics (1); the journals with the most are Computational Statistics (4), Japanese Journal of Statistics and Data Science (1) and Applied Intelligence (1).

By year#

20172025

Papers linking to microbenchmark (R package) by publication year
Year	Papers
2017	2
2018	1
2019	1
2021	1
2022	1
2023	2
2025	2

Journals#

Journals whose papers link to microbenchmark (R package) most
Journal	Papers
Computational Statistics	4
Japanese Journal of Statistics and Data Science	1
Applied Intelligence	1
Journal of Economic Dynamics and Control	1
Statistics and Computing	1

Papers linking to microbenchmark (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2018-12-27	Inducing non-orthogonal and non-linear decision boundaries in decision trees via interactive basis functions	Expert Systems with Applications	Antonio Páez, Fernando A. López Hernández, Manuel Ruiz Marín et al.	20	cites it
2017-09-13	A survival ensemble of extreme learning machine	Applied Intelligence	Hong Wang, Jianxin Wang, Lifeng Zhou	14	cites it
2022-08-10	Copula shrinkage and portfolio allocation in ultra-high dimensions	Journal of Economic Dynamics and Control	Stanislav Anatolyev, Пырлик Владимир Николаевич	6	cites it
2017-11-23	A least squares approach to latent variables extraction in formative–reflective models	Computational Statistics & Data Analysis	Marco Fattore, Matteo Maria Pelagatti, Giorgio Vittadini	3	cites it
2019-06-12	Computing expectations and marginal likelihoods for permutations	Computational Statistics	Ben Powell, Paul A. Smith	3	cites it
2023-06-10	Fast and consistent algorithm for the latent block model	Computational Statistics	Vincent Brault, Antoine Channarond	3	cites it
2023-07-01	hermiter: R package for sequential nonparametric estimation	Computational Statistics	Michael Stephanou, Melvin Varughese	1	cites it
2025-03-15	Estimation and model selection for finite mixtures of Tukey’s g- &-h distributions	Statistics and Computing	Tingting Zhan, Misung Yi, Amy R. Peck et al.	1	cites it
2021-08-05	Computing highest density regions for continuous univariate distributions with known probability functions	Computational Statistics	Ben O’Neill	0	cites it
2025-02-01	Fitting cure models using pseudo-observations approaches with the R package pseudoCure	Japanese Journal of Statistics and Data Science	Sy Han Chiou, Chien‐Lin Su, Feng‐Chang Lin	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). microbenchmark (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-microbenchmark

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

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