Evidence map›Paper›PMID 41122668›Full record

ArticleJournal of open source software2025

fastfrechet: An R package for fast implementation of Fréchet regression with distributional responses.

Alexander Coulter, Rebecca Lee, Irina Gaynanova

Abstract read
In one paragraph

Article in Journal of open source software, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Alexander CoulterDepartment of Statistics, Texas A&M University, United States.ORCID 0009-0001-9116-6588
Rebecca LeeDepartment of Statistics, Texas A&M University, United States.ORCID 0000-0001-8162-6555
Irina GaynanovaDepartment of Biostatistics, University of Michigan, United States.ORCID 0000-0002-4116-0268

Funding

New machine learning methods for extracting features from digital health data with applications to sleep apneaR01HL172785 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Irina Gaynanova · 2024 to 2026
$1.1M
NHLBI NIH HHS R01 HL172785
6 · The paper itself

Abstract

Distribution-as-response regression problems are gaining wider attention, especially within biomedical settings where observation-rich patient specific data sets are available, such as feature densities in CT scans (Petersen et al., 2021), actigraphy (Ghosal et al., 2023), and continuous glucose monitoring (Coulter et al., 2024; Matabuena et al., 2021). To accommodate the complex structure of such problems, Petersen & Müller (2019) proposed a regression framework called

Identifiers

PMID41122668
PMCPMC12536474

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.