Fast estimation of Gaussian Mixture Copula Models

The GMCM package offers R functions that perform high-dimensional meta-analysis (Li et. al., 2011) and general unsupervised cluster analysis (Tewari et. al., 2011) using Gaussian Copula Mixture Models in a very fast manner.

Gaussian copula mixture models (GMCMs) are a very flexible alternative to gaussian mixture models in unsupervised cluster analysis for continuous data where non-Gaussian clusters are present. GMCMs model the ranks of the observed data and are thus invariant to monotone increasing transformations of the data, i.e. they are semi-parametric and only the ordering of the data is important. Alternatively, a special-case of the GMCMs can be used for a novel meta-analysis approach in high-dimensional settings. In this context, the model tries to cluster results which agree and don't agree on statistical evidence into a reproducible and irreproducible group.

The optimization of the complicated likelihood function is difficult, however. The GMCM package utilizes Rcpp and RcppArmadillo to evaluate the likelihood function quickly and arrive at a parameter estimate using various optimization routines.

Additional information and documentation will follow. For now, run help("GMCM") in R for some help and examples.


The released and tested version of GMCM is available at CRAN (Comprehensive R Archive Network). It can be easily be installed from within R by running


If you wish to install the latest version of GMCM directly from the master branch here at GitHub, run


Note, that this version is in development and is different from the version at CRAN. As such, it may be unstable. Be sure that you have the package development prerequisites if you wish to install the package from the source.

When installed, run news(package = "GMCM") to view the latest notable changes of GMCM.