flamingos-package {flamingos} | R Documentation

## FLaMingos: Functional Latent datA Models for clusterING heterogeneOus curveS

### Description

`flamingos` is an open-source toolbox 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.

`flamingos` contains the following time series clustering and segmentation models:

- mixRHLP;

- mixHMM;

- mixHMMR.

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

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

### Author(s)

Maintainer: Florian Lecocq florian.lecocq@outlook.com (R port) [translator]

Authors:

- Faicel Chamroukhi faicel.chamroukhi@unicaen.fr (0000-0002-5894-3103)

- Marius Bartcus marius.bartcus@gmail.com (R port) [translator]

### References

Chamroukhi, Faicel, and Hien D. Nguyen. 2019. Model-Based Clustering and Classification of Functional Data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. https://chamroukhi.com/papers/MBCC-FDA.pdf.

Chamroukhi, F. 2016. Unsupervised Learning of Regression Mixture Models with Unknown Number of Components. Journal of Statistical Computation and Simulation 86 (November): 2308–34. https://chamroukhi.com/papers/Chamroukhi-JSCS-2015.pdf.

Chamroukhi, Faicel. 2016. Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation. Journal of Classification 33 (3): 374–411. https://chamroukhi.com/papers/Chamroukhi-PWRM-JournalClassif-2016.pdf.

Chamroukhi, F. 2015. Statistical Learning of Latent Data Models for Complex Data Analysis. Habilitation Thesis (HDR), Universite de Toulon. https://chamroukhi.com/Dossier/FChamroukhi-Habilitation.pdf.

Chamroukhi, F., H. Glotin, and A. Same. 2013. Model-Based Functional Mixture Discriminant Analysis with Hidden Process Regression for Curve Classification. Neurocomputing 112: 153–63. https://chamroukhi.com/papers/chamroukhi_et_al_neucomp2013a.pdf.

Chamroukhi, F., and H. Glotin. 2012. Mixture Model-Based Functional Discriminant Analysis for Curve Classification. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE, 1–8. Brisbane, Australia. https://chamroukhi.com/papers/Chamroukhi-ijcnn-2012.pdf.

Same, A., F. Chamroukhi, Gerard Govaert, and P. Aknin. 2011. Model-Based Clustering and Segmentation of Time Series with Changes in Regime. Advances in Data Analysis and Classification 5 (4): 301–21. https://chamroukhi.com/papers/adac-2011.pdf.

Chamroukhi, F., A. Same, P. Aknin, and G. Govaert. 2011. Model-Based Clustering with Hidden Markov Model Regression for Time Series with Regime Changes. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE, 2814–21. https://chamroukhi.com/papers/Chamroukhi-ijcnn-2011.pdf.

Chamroukhi, F., A. Same, G. Govaert, and P. Aknin. 2010. A Hidden Process Regression Model for Functional Data Description. Application to Curve Discrimination. Neurocomputing 73 (7-9): 1210–21. https://chamroukhi.com/papers/chamroukhi_neucomp_2010.pdf.

Chamroukhi, F. 2010. Hidden Process Regression for Curve Modeling, Classification and Tracking. Ph.D. Thesis, Universite de Technologie de Compiegne. https://chamroukhi.com/papers/FChamroukhi-Thesis.pdf.

### See Also

Useful links:

- https://github.com/fchamroukhi/FLaMingos

- Report bugs at https://github.com/fchamroukhi/FLaMingos/issues

[Package flamingos version 0.1.0 Index]