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.
|Author||Faicel Chamroukhi [aut] (<https://orcid.org/0000-0002-5894-3103>), Marius Bartcus [aut], Florian Lecocq [aut, cre]|
|Maintainer||Florian Lecocq <firstname.lastname@example.org>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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