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.


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Reference manual

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install.packages("samurais")

0.1.0 by Florian Lecocq, 4 months ago


https://github.com/fchamroukhi/SaMUraiS


Browse source code at https://github.com/cran/samurais


Authors: Faicel Chamroukhi [aut] , Marius Bartcus [aut] , Florian Lecocq [aut, cre]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports methods, stats, MASS, Rcpp

Suggests knitr, rmarkdown

Linking to Rcpp, RcppArmadillo


See at CRAN