High Dimensional Locally-Linear Mapping

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) ) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) < https://hal.archives-ouvertes.fr/hal-01347455>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) < https://arxiv.org/abs/1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).