Discover @fchamroukhi Open Source projects

Faïcel Chamroukhi (@fchamroukhi)

fchamroukhi

Stars
68
Global Rank 274,652 (Top 10 %)
Followers 43
Following 2
Registered almost 7 years ago
Most used languages

R
51.9 %

MATLAB
44.4 %

Python
3.7 %
Location 🇫🇷 France
Country Total Rank 6,873
Country Ranking

MATLAB
69

R
75

Python
7,491

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)

MEteorits

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

tMoE_m

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

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).

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

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.

PWR_m

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

RHLP_m

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

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).

MHMMR_m

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

mixHMMR

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

mixHMM

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

MixRHLP_m

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

SNMoE_m

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

tMoE

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

DECT-CLUST

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

PWR_R

Piecewise Regression (PWR) for Optimal Time Series Segmentation

HMMR_r

Hidden Markov Model Regression (HMMR) for Times Series Segmentation

mixHMM_m

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

MRHLP

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

StMoE_m

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

SNMoE

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

MRHLP_m

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

RHLP

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