Section: New Software and Platforms

MixtComp.V4

Keywords: Clustering - Statistics - Missing data - Mixed data

Functional Description: MixtComp (Mixture Computation) is a model-based clustering package for mixed data originating from the Modal team (Inria Lille). It has been engineered around the idea of easy and quick integration of all new univariate models, under the conditional independence assumption. New models will eventually be available from researches, carried out by the Modal team or by other teams. Currently, central architecture of MixtComp is built and functionality has been field-tested through industry partnerships. Five basic models (Gaussian, Multinomial, Poisson, Weibull, NegativeBinomial) are implemented, as well as two advanced models (Functional and Rank). MixtComp has the ability to natively manage missing data (completely or by interval). MixtComp is used as an R package, but its internals are coded in C++ using state of the art libraries for faster computation.

Release Functional Description: - New I/O system - Replacement of regex library - Improvement of initialization - Criteria for stopping the algorithm - Added management of partially missing data for several models - User documentation - Adding user features in R

• Participants: Christophe Biernacki, Vincent Kubicki, Matthieu Marbac-Lourdelle, Serge Iovleff, Quentin Grimonprez and Etienne Goffinet

• Partners: Université de Lille - CNRS

• Contact: Christophe Biernacki