Mixmod is a software package for Model-Based supervised and unsupervised classification on qualitative, quantitative and mixed data.

Available components:

• A package for R: Rmixmod
• A module for Python: Pymixmod
• A computational library: mixmodLib (C++)
• A Graphical User Interface: mixmodGUI

Main Statistical functionalities:

• Likelihood maximization with EM, CEM and SEM algorithm
• Parsimonious models
  • 14 models for quantitative data (Gaussian mixture models)
  • 5 models for qualitative data (Multinomial mixture models)
  • 20 models for mixed data (quantitative/qualitative)
  • 8 specific models for High Dimension
• Selection criteria: BIC, ICL, NEC, CV

Previous repository: https://gforge.inria.fr/projects/mixmod

• Folder structure
• C++ components
  • Requirements
  • Compilation
  • Examples
• Rmixmod
  • R Requirements
  • Build
• PyMixmod
• Docs
• Citation
• License
• Contributing

• Rmixmod R interface of the C++ mixmod library
• mixmodLib C++ mixmod library
• mixmodIOStream C++ library to manage IO
• mixmodCLI Command Line Interface
• Pymixmod Python interface
• mixmodGUI unmaintained
• mixmodMVC unmaintained

Requirements for mixmoLib:

sudo apt install -y cmake libeigen3-dev

Extra requirement for mixmodIOStream

sudo apt install -y libxml++2.6-dev

A main CMake file is used to compiled mixmodLib, mixmodIOStream and mixmodCLI.

Compilation options:

• -DCMAKE_INSTALL_PREFIX: installation folder
• -DCMAKE_BUILD_TYPE: Debug or Release (default)
• -DCMAKE_CXX_FLAGS: extra c++ compilation flags (optional)
• -DMIXMOD_BUILD_IOSTREAM: ON or OFF (default). Compile or not mixmodIOStream
• -DMIXMOD_BUILD_CLI: ON or OFF (default). Compile or not mixmodCLI. It requires -DMIXMOD_BUILD_IOSTREAM=ON
• -DMIXMOD_BUILD_EXAMPLES: ON or OFF (default)
• -DMIXMOD_ENABLE_OPENMP: OFF or ON (default). Enable OpenMP or not.

Generate makefile:

mkdir build
cd build
cmake -DCMAKE_INSTALL_PREFIX=~/usr/local/ -DCMAKE_CXX_FLAGS="-Wall -Wextra -D_GLIBCXX_ASSERTIONS" ..

Compile:

make install -j2

See mixmodLib/EXAMPLES for some examples.

R interface of the C++ mixmod library.

Install the following R packages in order to build Rmixmod:

install.packages(c("Rcpp", "RcppEigen", "devtools"))

In a terminal, run:

./build_rmixmod.sh

It creates a directory named Rmixmod_[version] containing the package archive.

The package can be installed running:

R CMD INSTALL Rmixmod_[version]/Rmixmod_[version].tar.gz

and checked running:

R CMD check --as-cran Rmixmod_[version]/Rmixmod_[version].tar.gz

Python interface of the C++ mixmod library.

See the dedicated README.

See the doc folder to find the different paper about Rmixmod, a statistical documentation and a user guide for mixmod.

Lebret, R., Iovleff, S., Langrognet, F., Biernacki, C., Celeux, G., & Govaert, G. (2015). Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library. Journal of Statistical Software, 67(6), 1–29. https://doi.org/10.18637/jss.v067.i06

See CITATION.bib

mixmod is distributed under the GPL v3 license

• Use the .clang-format file to format the c++ code.

• Use lintr to format the R code with the following .lintr file:

  linters: linters_with_defaults(
    line_length_linter(127),
    object_name_linter = NULL
    )