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 Blein–Nicolas et al (2024) <doi:10.1093/jrsssc/qlae012>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
| Version: |
2.3.1 |
| Imports: |
MASS, abind, corpcor, Matrix, igraph, capushe, glmnet, randomForest, e1071, mda, progress, mixOmics |
| Published: |
2026-03-02 |
| DOI: |
10.32614/CRAN.package.xLLiM |
| Author: |
Emeline Perthame [aut, cre] ([email protected]),
Florence Forbes [aut] ([email protected]),
Antoine Deleforge [aut] ([email protected]),
Emilie Devijver [aut] ([email protected]),
Melina Gallopin [aut] ([email protected]) |
| Maintainer: |
Emeline Perthame <emeline.perthame at pasteur.fr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
xLLiM results |
Documentation:
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Reverse dependencies:
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