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. These models are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?&tab=repositories&q=time-series&type=public&language=matlab>.

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

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NEWS

Peer review:

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

On CRAN:

artificial-intelligencechange-point-detectiondata-sciencedynamic-programmingem-algorithmhidden-markov-modelshidden-process-regressionhuman-activity-recognitionlatent-variable-modelsmodel-selectionmultivariate-timeseriesnewton-raphsonpiecewise-regressionstatistical-inferencestatistical-learningtime-series-analysistime-series-clustering

9 exports 12 stars 1.64 score 3 dependencies 28 scripts 114 downloads

Last updated 5 years agofrom:093a16b0b5. Checks:OK: 1 NOTE: 8. Indexed: yes.

Exports:emHMMRemMHMMRemMRHLPemRHLPfitPWRFisherselectHMMRselectMHMMRselectMRHLPselectRHLP

Dependencies:MASSRcppRcppArmadillo