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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.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: methods, stats, MASS, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2019-07-28
DOI: 10.32614/CRAN.package.samurais
Author: Faicel Chamroukhi ORCID iD [aut], Marius Bartcus [aut], Florian Lecocq [aut, cre]
Maintainer: Florian Lecocq <florian.lecocq at outlook.com>
License: GPL (≥ 3)
URL: https://github.com/fchamroukhi/SaMUraiS
NeedsCompilation: yes
Citation: samurais citation info
Materials: README
CRAN checks: samurais results

Documentation:

Reference manual: samurais.pdf
Vignettes: A-quick-tour-of-HMMR
A-quick-tour-of-MHMMR
A-quick-tour-of-MRHLP
A-quick-tour-of-PWR
A-quick-tour-of-RHLP
Model-selection-HMMR
Model-selection-MHMMR
Model-selection-MRHLP
Model-selection-RHLP

Downloads:

Package source: samurais_0.1.0.tar.gz
Windows binaries: r-devel: samurais_0.1.0.zip, r-release: samurais_0.1.0.zip, r-oldrel: samurais_0.1.0.zip
macOS binaries: r-release (arm64): samurais_0.1.0.tgz, r-oldrel (arm64): samurais_0.1.0.tgz, r-release (x86_64): samurais_0.1.0.tgz, r-oldrel (x86_64): samurais_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=samurais to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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