Provides a variety of original and flexible userfriendly statistical latent variable models and unsupervised learning algorithms to segment and represent timeseries 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 timeseries segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), PieceWise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references.
Package details 


Author  Faicel Chamroukhi [aut] (<https://orcid.org/0000000258943103>), 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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