-## Description
-
-The function selmix delivers a multivariate Gaussian mixture in regression model collection.
-According to the parameter estimation, we can compute classical model selection criterion, as BIC or AIC, or slope heuristic, using the CAPUSHE package.
-The methodology used is described in 'Model-Based Clustering for High-Dimensional Data. Application to Functional Data.',
-available at [this location](https://hal.archives-ouvertes.fr/hal-01060063)
-
-## Arguments
-
-Regressors, denoted by X (of size n x p) and responses, denoted by Y (of size n x q) are must-have arguments.
-
-Optionally, we could add
-
-* gamma: weight power in the Lasso penalty (according to Stadler et al., $\gamma \in \{0,1/2,1\}$;
-* mini: the minimum number of iterations;
-* maxi: the maximum number of iterations;
-* tau: the threshold for stopping EM algorithm;
-* kmin and kmax: the bounds of interesting number of components,
-* rangmin and rangmax: the bounds of interesting rank values.
+Re-writing from [a similar project](https://github.com/yagu0/select) in MATLAB, which
+corresponds to applied parts of the PhD thesis of both co-authors.