meteorits-package: MEteorits: Mixtures-of-ExperTs modEling for cOmplex and...

Description Author(s) References See Also


meteorits is a package containing several original and flexible mixtures-of-experts models to model, cluster and classify heteregenous data in many complex situations where the data are distributed according to non-normal and possibly skewed distributions, and when they might be corrupted by atypical observations. The toolbox also contains sparse mixture-of-experts models for high-dimensional data.

meteorits contains the following Mixture-of-Experts models:

For the advantages/differences of each of them, the user is referred to our mentioned paper references.

To learn more about meteorits, start with the vignettes: browseVignettes(package = "meteorits")


Maintainer: Florian Lecocq (R port) [translator]



Chamroukhi, F. 2017. Skew-T Mixture of Experts. Neurocomputing - Elsevier 266: 390–408.

Chamroukhi, F. 2016a. Robust Mixture of Experts Modeling Using the T-Distribution. Neural Networks - Elsevier 79: 20–36.

Chamroukhi, F. 2016b. Skew-Normal Mixture of Experts. In The International Joint Conference on Neural Networks (IJCNN). Vancouver, Canada.

Chamroukhi, F. 2015a. Non-Normal Mixtures of Experts.

Chamroukhi, F. 2015b. Statistical Learning of Latent Data Models for Complex Data Analysis. Habilitation Thesis (HDR), Universite de Toulon.

Chamroukhi, F. 2010. Hidden Process Regression for Curve Modeling, Classification and Tracking. Ph.D. Thesis, Universite de Technologie de Compiegne.

Chamroukhi, F., A. Same, G. Govaert, and P. Aknin. 2009. Time Series Modeling by a Regression Approach Based on a Latent Process. Neural Networks 22 (5-6): 593–602.

See Also

Useful links:

meteorits documentation built on Jan. 11, 2020, 9:13 a.m.