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samurais  

Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')
View on CRAN: Click here


Download and install samurais package within the R console
Install from CRAN:
install.packages("samurais")

Install from Github:
library("remotes")
install_github("cran/samurais")

Install by package version:
library("remotes")
install_version("samurais", "0.1.0")



Attach the package and use:
library("samurais")
Maintained by
Florian Lecocq
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-07-28
Latest Update: 2019-07-28
Description:
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.
How to cite:
Florian Lecocq (2019). samurais: Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS'). R package version 0.1.0, https://cran.r-project.org/web/packages/samurais. Accessed 10 Mar. 2026.
Previous versions and publish date:
0.1.0 (2019-07-28 11:50)
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Complete documentation for samurais
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