epertham/xLLiM: 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) <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).

Getting started

Package details

AuthorEmeline 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
MaintainerEmeline Perthame <emeline.perthame@pasteur.fr>
LicenseGPL (>= 2)
Version2.3
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("epertham/xLLiM")
epertham/xLLiM documentation built on Oct. 29, 2023, 6:16 a.m.