Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')
emHMMR implemens the EM (Baum-Welch) algorithm to fit a HMMR model.
emMHMMR implemens the EM (Baum-Welch) algorithm to fit a MHMMR model.
emMRHLP implemens the EM algorithm to fit a MRHLP model.
emRHLP implements the EM algorithm to fit a RHLP model.
fitPWRFisher implements an optimized dynamic programming algorithm to ...
hmmProcess calculates the probability distribution of a random process...
A Reference Class which represents multivariate data.
mkStochastic ensures that it is a stochastic vector, matrix or array.
A Reference Class which represents a fitted HMMR model.
A Reference Class which represents a fitted MHMMR model.
A Reference Class which represents a fitted MRHLP model.
A Reference Class which represents a fitted PWR model.
A Reference Class which represents a fitted RHLP model.
A Reference Class which contains parameters of a HMMR model.
A Reference Class which contains parameters of a MHMMR model.
A Reference Class which contains the parameters of a MRHLP model.
A Reference Class which contains the parameters of a PWR model.
A Reference Class which contains parameters of a RHLP model.
SaMUraiS: StAtistical Models for the UnsupeRvised segmentAtIon of time...
selectHMMR implements a model selection procedure to select an optimal...
selectMHMMR implements a model selection procedure to select an optima...
selecMRHLP implements a model selection procedure to select an optimal...
selecRHLP implements a model selection procedure to select an optimal ...
A Reference Class which contains statistics of a HMMR model.
A Reference Class which contains statistics of a MHMMR model.
A Reference Class which contains statistics of a MRHLP model.
A Reference Class which contains statistics of a PWR model.
A Reference Class which contains statistics of a RHLP model.
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.