MixtComp (Mixture Composer) is a model-based clustering package for mixed data originating from the Modal team (Inria Lille).
Mixture models parameters are estimated using a SEM algorithm. Five basic models (Gaussian, Multinomial, Poisson, Weibull, NegativeBinomial) are implemented to manage real, integer and categorical variables, as well as two advanced models (Func_CS for functional data and Rank_ISR for rank data). 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. 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 contributors. Currently, central architecture of MixtComp is built and functionality has been field-tested through industry partnerships.
master:
staging:
The following people contributed to the development of MixtComp: Vincent Kubicki, Christophe Biernacki, Quentin Grimonprez, Serge Iovleff, Matthieu Marbac-Lourdelle, Étienne Goffinet.
Copyrigth Inria - Université de Lille - CNRS
MixtComp is distributed under the AGPL 3.0 licence. For more details about the licences of MixtComp and its dependencies see the LICENCE.md file.
A description of the links between packages and external libraries can be found here in a text version and here in a visual version
Scientific papers about algorithm and models are available in the article folder.
There are two branches tested with github actions