samurais
Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')
Provides a variety of original and flexible user-friendly statistical latent variable models and unsupervised learning algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes. 'samurais' is built upon the following packages, each of them is an autonomous time-series segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references.
- Version0.1.0
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- samurais citation info
- Last release07/28/2019
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Team
Florian Lecocq
Faicel Chamroukhi
Marius Bartcus
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- Imports2 packages
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