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 [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:

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