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  "Type": "Package",
  "Title": "High Dimensional Locally-Linear Mapping",
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  "Author": "Emeline Perthame (emeline.perthame@inria.fr), Florence Forbes\n(florence.forbes@inria.fr), Antoine Deleforge\n(antoine.deleforge@inria.fr), Emilie Devijver\n(emilie.devijver@kuleuven.be), Melina Gallopin\n(melina.gallopin@u-psud.fr)",
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  "Description": "Provides a tool for non linear mapping (non linear\nregression) using a mixture of regression model and an inverse\nregression strategy. The methods include the GLLiM model (see\nDeleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based\non Gaussian mixtures and a robust version of GLLiM, named SLLiM\n(see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>)\nbased on a mixture of Generalized Student distributions. The\nmethods also include BLLiM (see Devijver et al (2017)\n<arXiv:1701.07899>) which is an extension of GLLiM with a\nsparse block diagonal structure for large covariance matrices\n(particularly interesting for transcriptomic data).",
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  "biocViews": "mixOmics",
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