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).
| Version: | 2.1 |
| Imports: | MASS, abind, corpcor, Matrix, igraph, capushe |
| Suggests: | shock |
| Published: | 2017-05-23 |
| Author: | Emeline 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) |
| Maintainer: | Emeline Perthame <emeline.perthame at pasteur.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | xLLiM results |
| Reference manual: | xLLiM.pdf |
| Package source: | xLLiM_2.1.tar.gz |
| Windows binaries: | r-devel: xLLiM_2.1.zip, r-release: xLLiM_2.1.zip, r-oldrel: xLLiM_2.1.zip |
| macOS binaries: | r-release: xLLiM_2.1.tgz, r-oldrel: xLLiM_2.1.tgz |
| Old sources: | xLLiM archive |
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