MOdels for Data Analysis and Learning

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The Modal Project-Team

Research themes

  • Data analysis: clustering and visualization.
  • Data learning: supervised and semi-supervised classification, aggregation methods…
  • Complex data: nominal covariates, heterogeneous data, functional data, rank data, ordinal data…
  • Model-free learning: PAC-Bayesian theory.

Check out the institutional webpage (leading to activity reports)


  • Genomic studies and Biology
  • Finance, Marketing and Business Analytics
  • Atmospheric and natural sciences
  • Data mining and Information retrieval



  • Pycobra: Python library for ensemble learning (both in classification and regression), and also includes several visualisation tools (such as Voronoi tessellation builders).
  • BlockCluster: R package based on rtkore for simultaneous clustering of rows and columns. It can be downloaded on CRAN blockcluster.
  • CorReg: R package for linear regression with correlated variables.
  • MixtComp: Clustering of heterogeneous data with missing values. Demonstrator available on MASSICCC.
  • RankCluster: R package for clustering of Rank Data.
  • Rmixmod: R package for the clustering and classification of Gaussian and Multinomial data.
  • MixAll R package for clustering of mixed data with missing values.
  • rtkore A binding using Rcpp of the STK++ library is available on CRAN.
  • STK++: Library written in C++ for creation of statistics and/or data mining console programs. More documentation is available here.

Try it online!

start.txt · Last modified: 2019/04/03 12:11 by bguedj