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) ) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).


Reference manual

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2.2 by Emeline Perthame, a year ago

Browse source code at https://github.com/cran/xLLiM

Authors: Emeline Perthame ([email protected]) , Florence Forbes ([email protected]) , Antoine Deleforge ([email protected]) , Emilie Devijver ([email protected]) , Melina Gallopin ([email protected])

Documentation:   PDF Manual  

GPL (>= 2) license

Imports MASS, abind, corpcor, Matrix, igraph, capushe, glmnet, randomForest, e1071, mda, progress

Suggests shock

See at CRAN