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samurais
View on CRAN: Click
here
Download and install samurais package within the R console
Install from CRAN:
install.packages("samurais")
Install from Github:
library("remotes")
install_github("cran/samurais")
Install by package version:
library("remotes")
install_version("samurais", "0.1.0")
Attach the package and use:
library("samurais")
Maintained by
Florian Lecocq
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.samurais . samurais citation info . samurais results . samurais.pdf . samurais_0.1.0.tar.gz . samurais_0.1.0.zip . samurais_0.1.0.zip . samurais_0.1.0.zip . samurais_0.1.0.tgz . samurais_0.1.0.tgz . samurais_0.1.0.tgz . samurais_0.1.0.tgz . samurais_0.1.0.tgz . samurais_0.1.0.tgz . https://CRAN.R-project.org/package=samurais .
First Published: 2019-07-28
Latest Update: 2019-07-28
Description:
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.
How to cite:
Florian Lecocq (2019). samurais: Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS'). R package version 0.1.0, https://cran.r-project.org/web/packages/samurais. Accessed 05 Apr. 2025.
Previous versions and publish date:
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Other R packages that samurais depends,
imports, suggests or enhances
Complete documentation for samurais
Functions, R codes and Examples using
the samurais R package
Some associated functions: MData-class . ModelHMMR-class . ModelMHMMR-class . ModelMRHLP-class . ModelPWR-class . ModelRHLP-class . ParamHMMR-class . ParamMHMMR-class . ParamMRHLP-class . ParamPWR-class . ParamRHLP-class . StatHMMR-class . StatMHMMR-class . StatMRHLP-class . StatPWR-class . StatRHLP-class . emHMMR . emMHMMR . emMRHLP . emRHLP . fitPWRFisher . hmmProcess . mkStochastic . multivrealdataset . multivtoydataset . samurais-package . selectHMMR . selectMHMMR . selectMRHLP . selectRHLP . univrealdataset . univtoydataset .
Some associated R codes: MData.R . ModelHMMR.R . ModelMHMMR.R . ModelMRHLP.R . ModelPWR.R . ModelRHLP.R . ParamHMMR.R . ParamMHMMR.R . ParamMRHLP.R . ParamPWR.R . ParamRHLP.R . RcppExports.R . StatHMMR.R . StatMHMMR.R . StatMRHLP.R . StatPWR.R . StatRHLP.R . data-multivrealdataset.R . data-multivtoydataset.R . data-univrealdataset.R . data-univtoydataset.R . dynamicProg.R . emHMMR.R . emMHMMR.R . emMRHLP.R . emRHLP.R . fitPWRFisher.R . hmmProcess.R . mkStochastic.R . samurais-package.R . selectHMMR.R . selectMHMMR.R . selectMRHLP.R . selectRHLP.R . utils.R . Full samurais package functions and examples
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