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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "funMoDisco" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'funMoDisco: Motif Discovery in Functional Data'
version: 1.1.5
doi: 10.1080/10618600.2022.2156522
identifiers:
- type: doi
  value: 10.32614/CRAN.package.funMoDisco
abstract: 'Efficiently implementing two complementary methodologies for discovering
  motifs in functional data: ProbKMA and FunBIalign. Cremona and Chiaromonte (2023)
  "Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in
  Functional Data" <https://doi.org/10.1080/10618600.2022.2156522> is a probabilistic
  K-means algorithm that leverages local alignment and fuzzy clustering to identify
  recurring patterns (candidate functional motifs) across and within curves, allowing
  different portions of the same curve to belong to different clusters. It includes
  a family of distances and a normalization to discover various motif types and learns
  motif lengths in a data-driven manner. It can also be used for local clustering
  of misaligned data. Di Iorio, Cremona, and Chiaromonte (2023) "funBIalign: A Hierarchical
  Algorithm for Functional Motif Discovery Based on Mean Squared Residue Scores" <https://doi.org/10.48550/arXiv.2306.04254>
  applies hierarchical agglomerative clustering with a functional generalization of
  the Mean Squared Residue Score to identify motifs of a specified length in curves.
  This deterministic method includes a small set of user-tunable parameters. Both
  algorithms are suitable for single curves or sets of curves. The package also includes
  a flexible function to simulate functional data with embedded motifs, allowing users
  to generate benchmark datasets for validating and comparing motif discovery methods.'
authors:
- family-names: Cremona
  given-names: Marzia Angela
- family-names: Chiaromonte
  given-names: Francesca
- family-names: Di Iorio
  given-names: Jacopo
  email: jacopo.di.iorio@emory.edu
- family-names: Feresini
  given-names: Niccolò
- family-names: Lazzarini
  given-names: Riccardo
preferred-citation:
  type: article
  title: Probabilistic K-means with Local Alignment for Clustering and Motif Discovery
    in Functional Data
  authors:
  - family-names: Cremona
    given-names: Marzia A.
  - family-names: Chiaromonte
    given-names: Francesca
  journal: Journal of Computational and Graphical Statistics
  volume: '32'
  issue: '3'
  year: '2023'
  publisher:
    name: Taylor & Francis
  doi: 10.1080/10618600.2022.2156522
  url: https://doi.org/10.1080/10618600.2022.2156522
  start: 1119-1130
repository: https://cran.r-universe.dev
commit: 9f85951e9506458197391ee292a73ed3472fc49b
date-released: '2026-04-21'
contact:
- family-names: Di Iorio
  given-names: Jacopo
  email: jacopo.di.iorio@emory.edu
references:
- type: generic
  title: 'funBIalign: A Hierarchical Algorithm for Functional Motif Discovery Based
    on Mean Squared Residue Scores'
  authors:
  - family-names: Di Iorio
    given-names: Jacopo
  - family-names: Cremona
    given-names: Marzia A.
  - family-names: Chiaromonte
    given-names: Francesca
  year: '2023'
  url: https://arxiv.org/abs/2306.04254