CRAN/E | samurais

samurais

Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

Installation

About

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.

Citation samurais citation info
github.com/fchamroukhi/SaMUraiS

Key Metrics

Version 0.1.0
R ≥ 2.10
Published 2019-07-28 1900 days ago
Needs compilation? yes
License GPL (≥ 3)
CRAN checks samurais results

Downloads

Yesterday 2 +100%
Last 7 days 15 -44%
Last 30 days 106 -9%
Last 90 days 333 -16%
Last 365 days 1.633 +1%

Maintainer

Maintainer

Florian Lecocq

Authors

Faicel Chamroukhi

aut

Marius Bartcus

aut

Florian Lecocq

aut / cre

Material

README
Reference manual
Package source

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

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 2.10

Imports

methods
stats
MASS
Rcpp

Suggests

knitr
rmarkdown

LinkingTo

Rcpp
RcppArmadillo