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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.
Getting started
Browse package contents
Package details |
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| Author | Faicel Chamroukhi [aut] (<https://orcid.org/0000-0002-5894-3103>), Marius Bartcus [aut], Florian Lecocq [aut, cre] |
| Maintainer | Florian Lecocq <florian.lecocq@outlook.com> |
| License | GPL (>= 3) |
| Version | 0.1.0 |
| URL | https://github.com/fchamroukhi/SaMUraiS |
| Package repository | View on CRAN |
| Installation |
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