ProbKMA: Implements a probabilistic K-means algorithm that leverages local alignment and fuzzy clustering to discover recurring patterns (functional motifs) within and across curves.
Capable of handling diverse motifs through a family of distances and normalization techniques.
Learns motif lengths in a data-driven manner and supports local clustering for misaligned data.
FunBIalign: Provides hierarchical agglomerative clustering using the Mean Squared Residue Score for motif identification of specified lengths in functional data.
Offers a more deterministic approach with user-tunable parameters for control over motif detection.
Simulation Tools: Includes functions to simulate functional data embedded with motifs, enabling users to create benchmark datasets for validating and comparing motif discovery methods.