The main focus of MODAL is to design generative models dealing with complex multivariate and/or heterogeneous data. Typical instances of such data are
- nominal covariables for the multivariate setting,
- and the combination of continuous and nominal variables for the heterogeneous setting.
Obviously, other widespread complex covariables are of interest such as ordinal, ranks, and intervals data.
From these generative models, a convenient and efficient statistical analysis remains to be carried out, leading to data analysis (visualization, clustering) and data learning (supervised and semi-supervised classification, density estimation).