FoReco (R package): papers linking to it

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

← Code and data repositories cited in research papers

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

3 papers in the tracked journals link to FoReco (R package) in their Crossref records, the first in 2021; most of them (1) in 2023.
By category: AI (2) and Operations Research (1); the journals with the most are Expert Systems with Applications (2) and Computers & Industrial Engineering (1).
2 of the papers build on one another: a later paper cites an earlier one that also links FoReco (R package).

By year#

20212023

Papers linking to FoReco (R package) by publication year
Year	Papers
2021	1
2022	1
2023	1

Journals#

Journals whose papers link to FoReco (R package) most
Journal	Papers
Expert Systems with Applications	2
Computers & Industrial Engineering	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Hierarchical container throughput forecasting: The value of coherent forecasts in the management of ports operations Computers & Industrial Engineering, 2022
- Forecast reconciliation in the temporal hierarchy: Special case of intermittent demand with obsolescence Expert Systems with Applications, 2023 — cites the paper above

Papers linking to FoReco (R package), most cited first
Published	Title	Journal	Authors	Citations	Link
2021-05-02	A machine learning approach for forecasting hierarchical time series	Expert Systems with Applications	Paolo Mancuso, Veronica Piccialli, Antonio M. Sudoso	74	cites it
2022-09-14	Hierarchical container throughput forecasting: The value of coherent forecasts in the management of ports operations	Computers & Industrial Engineering	Kamal Sanguri, Sonali Shankar, Sushil Punia et al.	18	cites it
2023-01-20	Forecast reconciliation in the temporal hierarchy: Special case of intermittent demand with obsolescence	Expert Systems with Applications	Kamal Sanguri, Sabyasachi Patra, Sushil Punia	5	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). FoReco (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-foreco

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

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