Motif Discovery in Functional Data
Overview
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" doi: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" doi: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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
Show 3 earlier changesHide 3 earlier changes
- NOTE11 OK, 1 NOTE2026-04-25
- ERROR10 OK, 3 NOTE, 1 ERROR2026-04-18
- NOTE11 OK, 3 NOTE2026-03-10
Documentation
- Help pages
- 46includes a package overview page
- Examples that run
- 0%8 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 11%
- Documented parameters
- 99%
- Return-value docs
- 97%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing funMoDisco adds 69 packages (40 with compiled code, 64 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.1.5Latest
- 1.1.02025-11-07 · diff ↗ ·
removed 2 exports
generateCurves, motifSimulationAppadded 35 exports
.check_fits, .diss_d0_d1_L2, .domain, .find_min_diss, .find_occurrences, .generate_coefficients, .mapply_custom, .resample, .select_domain, .transform_list, .transform_to_matrix, ProbKMA, add_error_to_motif, add_motif, cluster_candidate_motifs, cluster_candidate_motifs_plot, compare_nodes, filter_candidate_motifs, find_recommended_path, generate_background_curve, generate_curve_vector, get_accolites, get_minidend, get_parents, get_path_complete, initialChecks, motifs_search, motifs_search_plot, padding, probKMA_plot and 5 more - 1.0.02025-04-15
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-04-15
- Total releases
- 3 / 1 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 4.4 MB / 2 files
- Download size
- 4.8 MB
Cite
Cite this package
Run in R for the authors' entry:
citation("funMoDisco")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1080/10618600.2022.2156522.
Cite the R Observatory
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From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.