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
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

Downloads:

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
Package source: samurais_0.1.0.tar.gz
Windows binaries: r-devel: samurais_0.1.0.zip, r-devel-gcc8: samurais_0.1.0.zip, r-release: samurais_0.1.0.zip, r-oldrel: samurais_0.1.0.zip
OS X binaries: r-release: samurais_0.1.0.tgz, r-oldrel: samurais_0.1.0.tgz

Linking:

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