Faïcel Chamroukhi

SaMUraiS. StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

HMMR. Hidden Markov Model Regression (HMMR) for time-series segmentation

FLaMingos. Functional Latent datA Models for clusterING heterogeneOus curveS

MHMMR. Joint segmentation of multivariate time series with a Multiple Hidden Markov Model Regression (MHMMR)

tMoE_m. Robust Mixtures-of-Experts modelling using the t distribution for clustering and non-linear regression for heteregenous data

mixHMMR_m. Clustering and segmentation of heterogeneous functional data (sequential data) with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm

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

mixRHLP_py. A flexible mixture model for simultaneous clustering and segmentation of functional data (time series). It uses the EM algorithm (or a CEM-like algorithm).

MHMMR_m. Joint segmentation of multivariate time-series with a Multiple Hidden Markov Model Regression (MHMMR)

mixHMM. Clustering and segmentation of time series by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm

PWR_m. Piecewise regression (PWR) for the optimal segmentation of time-series with regime changes

HMMR_r. Hidden Markov Model Regression (HMMR) for Times Series Segmentation

mixRHLP. A flexible mixture model for simultaneous clustering and segmentation of functional data (time series). It uses the EM algorithm (or a CEM-like algorithm).

StMoE. Robust modeling, density estimation and model-based clustering of heterogeneous regression data with possibly skewed and non-normal distributions using skew-t mixture of experts.

RHLP_m. User-friendly and flexible algorithm for time-series segmentation by a Regression model with a Hidden Logistic Process (RHLP).

mixHMMR. Clustering and segmentation of time series with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm

MixRHLP_m. Flexible Mixture modelling for simultaneous clustering and segmentation of heterogeneous functional data

mixHMM_m. Clustering and segmentation of heteregeneous functional data (sequential data) by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm

MRHLP_m. Joint segmentation of multivariate time-series with a Multiple Regression model with a Hidden Logistic Process (MRHLP).

tMoE. Robust Mixtures-of-Experts for Non-Linear Regression and Clustering

RHLP. User-freindly and flexible algorithm for time series segmentation by a Regression model with a Hidden Logistic Process (RHLP).

MRHLP. Joint segmentation of multivariate time series with a Multiple Regression model with a Hidden Logistic Process (MRHLP).

PWR_R. Piecewise Regression (PWR) for Optimal Time Series Segmentation

SNMoE. Skew-Normal Mixture-of-Experts: A toolbox for Non-Linear Regression and Clustering using some non-normal mixtures of experts

StMoE_m. Toolbox for the Skew-t mixture of experts (StMoE) model

SNMoE_m. Skew-Normal Mixture-of-Experts: A toolbox for Non-Linear Regression and Clustering using some non-normal mixtures of experts

DECT-CLUST. DECT-CLUST: DECT image clustering and application to HNSCC tumor segmentation