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.

Package details

AuthorFaicel Chamroukhi [aut] (<https://orcid.org/0000-0002-5894-3103>), Marius Bartcus [aut], Florian Lecocq [aut, cre]
MaintainerFlorian Lecocq <florian.lecocq@outlook.com>
LicenseGPL (>= 3)
Version0.1.0
URL https://github.com/fchamroukhi/SaMUraiS
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("samurais")

Try the samurais package in your browser

Any scripts or data that you put into this service are public.

samurais documentation built on July 28, 2019, 5:02 p.m.