Package: samurais 0.1.0

Florian Lecocq

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.

Authors:Faicel Chamroukhi [aut], Marius Bartcus [aut], Florian Lecocq [aut, cre]

samurais_0.1.0.tar.gz
samurais_0.1.0.tar.gz(r-4.5-noble)samurais_0.1.0.tar.gz(r-4.4-noble)
samurais_0.1.0.tgz(r-4.4-emscripten)samurais_0.1.0.tgz(r-4.3-emscripten)
samurais.pdf |samurais.html✨
samurais/json (API)

# Install 'samurais' in R:
install.packages('samurais', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/fchamroukhi/samurais/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • multivrealdataset - Time series representing the three acceleration components recorded over time with body mounted accelerometers during the activity of a given person.
  • multivtoydataset - A simulated non-stationary multidimensional time series with regime changes.
  • univrealdataset - Time series representing the electrical power consumption during a railway switch operation
  • univtoydataset - A simulated non-stationary time series with regime changes.

4.10 score 28 scripts 120 downloads 9 exports 3 dependencies

Last updated 5 years agofrom:2eae2daafb. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKSep 19 2024
R-4.5-linux-x86_64NOTESep 19 2024

Exports:emHMMRemMHMMRemMRHLPemRHLPfitPWRFisherselectHMMRselectMHMMRselectMRHLPselectRHLP

Dependencies:MASSRcppRcppArmadillo

A-quick-tour-of-HMMR

Rendered fromA-quick-tour-of-HMMR.Rmdusingknitr::rmarkdownon Sep 19 2024.

Last update: 2019-07-28
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A-quick-tour-of-MHMMR

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A-quick-tour-of-MRHLP

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A-quick-tour-of-PWR

Rendered fromA-quick-tour-of-PWR.Rmdusingknitr::rmarkdownon Sep 19 2024.

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A-quick-tour-of-RHLP

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Model-selection-HMMR

Rendered fromModel-selection-HMMR.Rmdusingknitr::rmarkdownon Sep 19 2024.

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Model-selection-MHMMR

Rendered fromModel-selection-MHMMR.Rmdusingknitr::rmarkdownon Sep 19 2024.

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Model-selection-MRHLP

Rendered fromModel-selection-MRHLP.Rmdusingknitr::rmarkdownon Sep 19 2024.

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Model-selection-RHLP

Rendered fromModel-selection-RHLP.Rmdusingknitr::rmarkdownon Sep 19 2024.

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Readme and manuals

Help Manual

Help pageTopics
SaMUraiS: StAtistical Models for the UnsupeRvised segmentAtIon of time-Seriessamurais-package samurais
emHMMR implemens the EM (Baum-Welch) algorithm to fit a HMMR model.emHMMR
emMHMMR implemens the EM (Baum-Welch) algorithm to fit a MHMMR model.emMHMMR
emMRHLP implemens the EM algorithm to fit a MRHLP model.emMRHLP
emRHLP implements the EM algorithm to fit a RHLP model.emRHLP
fitPWRFisher implements an optimized dynamic programming algorithm to fit a PWR model.fitPWRFisher
hmmProcess calculates the probability distribution of a random process following a Markov chainhmmProcess
A Reference Class which represents multivariate data.MData MData-class
mkStochastic ensures that it is a stochastic vector, matrix or array.mkStochastic
A Reference Class which represents a fitted HMMR model.ModelHMMR ModelHMMR-class
A Reference Class which represents a fitted MHMMR model.ModelMHMMR ModelMHMMR-class
A Reference Class which represents a fitted MRHLP model.ModelMRHLP ModelMRHLP-class
A Reference Class which represents a fitted PWR model.ModelPWR ModelPWR-class
A Reference Class which represents a fitted RHLP model.ModelRHLP ModelRHLP-class
Time series representing the three acceleration components recorded over time with body mounted accelerometers during the activity of a given person.multivrealdataset
A simulated non-stationary multidimensional time series with regime changes.multivtoydataset
A Reference Class which contains parameters of a HMMR model.ParamHMMR ParamHMMR-class
A Reference Class which contains parameters of a MHMMR model.ParamMHMMR ParamMHMMR-class
A Reference Class which contains the parameters of a MRHLP model.ParamMRHLP ParamMRHLP-class
A Reference Class which contains the parameters of a PWR model.ParamPWR ParamPWR-class
A Reference Class which contains parameters of a RHLP model.ParamRHLP ParamRHLP-class
selectHMMR implements a model selection procedure to select an optimal HMMR model with unknown structure.selectHMMR
selectMHMMR implements a model selection procedure to select an optimal MHMMR model with unknown structure.selectMHMMR
selecMRHLP implements a model selection procedure to select an optimal MRHLP model with unknown structure.selectMRHLP
selecRHLP implements a model selection procedure to select an optimal RHLP model with unknown structure.selectRHLP
A Reference Class which contains statistics of a HMMR model.StatHMMR StatHMMR-class
A Reference Class which contains statistics of a MHMMR model.StatMHMMR StatMHMMR-class
A Reference Class which contains statistics of a MRHLP model.StatMRHLP StatMRHLP-class
A Reference Class which contains statistics of a PWR model.StatPWR StatPWR-class
A Reference Class which contains statistics of a RHLP model.StatRHLP StatRHLP-class
Time series representing the electrical power consumption during a railway switch operationunivrealdataset
A simulated non-stationary time series with regime changes.univtoydataset