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) <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).
|Author||Emeline Perthame ([email protected]), Florence Forbes ([email protected]), Antoine Deleforge ([email protected]), Emilie Devijver ([email protected]), Melina Gallopin ([email protected])|
|Maintainer||Emeline Perthame <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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