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SelvarMix: Regularization for Variable Selection in Model-Based Clustering and Discriminant Analysis
Performs a regularization approach to variable selection in the model-based clustering and classification frameworks. First, the variables are arranged in order with a lasso-like procedure. Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011) <doi:10.1016/j.csda.2009.04.013>, <doi:10.1016/j.jmva.2011.05.004> is adapted to define the role of variables in the two frameworks.
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Package details |
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| Author | Mohammed Sedki, Gilles Celeux, Cathy Maugis-Rabusseau |
| Maintainer | Mohammed Sedki <mohammed.sedki@u-psud.fr> |
| License | GPL (>= 3) |
| Version | 1.2.1 |
| Package repository | View on CRAN |
| Installation |
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