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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.1.0 by Florian Lecocq, 2 years ago

Browse source code at

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