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:
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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