!page under construction!

SaMUraiS:
StAtistical Models for the UnsupeRvised segmentAtIon of time-Series.

SaMUraiS is an open source toolbox (available in R and in Matlab) including many original and flexible user-friendly statistical latent variable models and unsupervised algorithms to segment and represent, time-series data (univariate or multivariate), and more generally, longitudinal data which include regime changes. Our samurais use mainly the following efficient ?sword? packages to segment data: RHLP; HMM/HMMR; PWR; MRHLP; MHMMR. R Software  Matlab Software

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FLaMingoS:
Functional Latent datA Models for clusterING heterogeneOus curveS

FLaMingoS is an open source toolbox (available in R and in Matlab) for the simultaneous clustering (or classification) and segmentation of heterogeneous functional data (i.e time-series ore more generally longitudinal data), with original and flexible functional latent variable models, fitted by unsupervised algorithms, including EM algorithms. Our nice FLaMingoS are mainly: mixRHLP, mixHMM, mixHMMR, PWRM, MixReg, unsupMixReg. R Software  Matlab Software

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MEteorits:
Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS

MEteoritS is an open source toolbox (available in R and in Matlab) containing several original and flexible mixtures-of-experts models to model, cluster and classify heterogeneous data in many complex situations where the data are distributed according non-normal, possibly skewed distributions, and when they might be corrupted by atypical observations. The toolbox contains in particular sparse mixture-of-experts models for high-dimensional data. Our (dis-)covered meteorites are for instance the following: NMoE, NNMoE, tMoE, StMoE, SNMoE, RMoE. R Software  Matlab Software

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Time series segmentation

Flexible friendly user time series segmentation approaches:

Regression with hidden logistic process (RHLP) .m .R
Hidden Markov model regression (HMMR) .m .R
Piecewise regression (PWR) .m .R

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Robust Mixture-of-Experts

t Mixture-of-Experts >> TMoE

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Functional Data Clustering and Segmentation
using flexible Functional Mixture Models:

Mixture of regressions with hidden logistic processes >> MixRHLP
Mixture of hidden Markov models >> MixHMM
Mixture of hidden Markov model regressions >> MixHMMR
Piece-wise regression mixture model >> PWRM

and standard Functional Mixture Models:

Mixture of regressions >> MixRreg
Mixture of spline regressions >> PSRM and B-spline regressions >> bSPRM

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Multivariate time series segmentation for
humain activity recognition

Multivariate regression with hidden logistic process >> MRHLP
Multivariate hidden Markov model regression >> MHMMR

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Fully unsupervised non-parametric signal decomposition

Bayesian sparse non-parametric Dirichlet process mixture models >> DPPM

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Bayesian spatial functional data clustering

Bayesian mixture of spatial spline regressions for functional data clustering >> BSSRM

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Bayesian Non-Parametric Clustering

Fully unsupervised and sparce clustering of high-dimensional data using Dirichlet Process Parsimonious Mixtures >> DPPM

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Funlly unsupervised regression mixtures for functionald data clustering

Unsupervised learning of regression mixture models with unknown number of components:
Robust EM and regression mixture >> RobustEM-PRM
Robust EM and spline regression mixture >> RobustEM-SRM
Robust EM and B-spline regression mixture >> RobustEM-bSRM

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Unsupervised regularised MLE for high-dimensional regression/clustering

Regularized mixtures-of-experts and a hybrib EM/MM algorithm >> RMoE-EM/MM
Regularized mixtures-of-experts and a hybrib EM/Proximal Newton algorithm >> RMoE-EM/PLN

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Non-normal and robust mixtures-of-experts (NNMoE)
Skew-Normal Mixture-of-Experts >> SNMoE
t Mixture-of-Experts >> TMoE
Skew-t Mixture-of-Experts >> STMoE
Here are the source codes of what I've developed/'m developing during my research! (list)
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