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.

Package details

AuthorFaicel Chamroukhi [aut] (<>), Marius Bartcus [aut], Florian Lecocq [aut, cre]
MaintainerFlorian Lecocq <>
LicenseGPL (>= 3)
Package repositoryView on CRAN
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samurais documentation built on July 28, 2019, 5:02 p.m.