SuperLearner (R package): papers linking to it

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

← Code and data repositories cited in research papers

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

6 papers in the tracked journals link to SuperLearner (R package) in their Crossref records, the first in 2018; most of them (1) in 2026.
By category: Statistics (3) and AI (3); the journals with the most are Expert Systems with Applications (2), Computational Statistics (1) and Journal of the Korean Statistical Society (1).

By year#

20182026

Papers linking to SuperLearner (R package) by publication year
Year	Papers
2018	1
2020	1
2022	1
2023	1
2024	1
2026	1

Journals#

Journals whose papers link to SuperLearner (R package) most
Journal	Papers
Expert Systems with Applications	2
Computational Statistics	1
Journal of the Korean Statistical Society	1
Neural Computing and Applications	1
Statistical Papers	1

Papers linking to SuperLearner (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2020-07-06	Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms	Neural Computing and Applications	Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis	177	cites it
2022-01-01	Predictive Model of Employee Attrition Based on Stacking Ensemble Learning	Expert Systems with Applications	Doohee Chung, Jinseop Yun, Jeha Lee et al.	100	cites it
2023-07-13	A hybrid machine learning framework for forecasting house price	Expert Systems with Applications	Choujun Zhan, Yonglin Liu, Zeqiong Wu et al.	51	cites it
2018-09-14	When and when not to use optimal model averaging	Statistical Papers	Michael Schomaker, Christian Heumann	7	cites it
2024-09-23	Forecasting the cost of drought events in France by Super Learning from a short time series of many slightly dependent data	Computational Statistics	Geoffrey Ecoto, Aurélien Bibaut, Antoine Chambaz	1	cites it
2026-09-23	Robust estimation of individualized quantile treatment effects in high dimensions	Journal of the Korean Statistical Society	Seohyeon Park, Minji Sung, Sangbum Choi	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). SuperLearner (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-superlearner

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

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