Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
Package details |
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Author | Emeline Perthame (emeline.perthame@inria.fr), Florence Forbes (florence.forbes@inria.fr), Antoine Deleforge (antoine.deleforge@inria.fr), Emilie Devijver (emilie.devijver@kuleuven.be), Melina Gallopin (melina.gallopin@u-psud.fr) |
Bioconductor views | mixOmics |
Maintainer | Emeline Perthame <emeline.perthame@pasteur.fr> |
License | GPL (>= 2) |
Version | 2.3 |
Package repository | View on GitHub |
Installation |
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