A generative art system for producing tree-like
    images using an L-system to create the structures. The package 
    includes tools for generating the data structures and visualise
    them in a variety of styles.

Provides a variety of original and flexible user-friendly 
    statistical latent variable models for the simultaneous clustering and 
    segmentation of heterogeneous functional data (i.e time series, or more 
    generally longitudinal data, fitted by unsupervised algorithms, including 
    EM algorithms. Functional Latent Data Models for Clustering heterogeneous 
    curves ('FLaMingos') are originally introduced and written in 'Matlab' by
    Faicel Chamroukhi 
    <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. 
    The references are mainly the following ones.
    Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>.
    Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>.
    Chamroukhi F., Same A., Aknin P. and Govaert G. (2011). <doi:10.1109/IJCNN.2011.6033590>.
    Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>.
    Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>.
    Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>.
    Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>.
    Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.

Tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017)  <doi:10.32614/RJ-2017-029>.

The Forecast Linear Augmented Projection (flap) method reduces 
    forecast variance by adjusting the forecasts of multivariate time series to 
    be consistent with the forecasts of linear combinations (components) of the 
    series by projecting all forecasts onto the space where the linear 
    constraints are satisfied. The forecast variance can be reduced 
    monotonically by including more components. For a given number of 
    components, the flap method achieves maximum forecast variance reduction 
    among linear projections.