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Algorithms for (i) unsupervised learning for dataset with missing data and/or left-censored data, using multiple imputation and consensus clustering ; (ii) semi-supervised learning with a survival endpoint (right-censored) for complete or incomplete datasets, using multiple imputation and consensus clustering in the latter case. The methods are described in Faucheux et al. (2021) <doi:10.1002/bimj.201900366> and Faucheux et al. (2021) <doi:10.1002/bimj.202000365>, respectively.
Package details |
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Author | Lilith Faucheux [aut, cre], Sylvie Chevret [ths], Matthieu Resche-Rigon [ctb], Marie Perrot-Dockès [ctb], Eric Han [ctb] |
Maintainer | Lilith Faucheux <lilith.faucheux@inserm.fr> |
License | GPL (>= 3) |
Version | 1.0.1 |
URL | https://github.com/LilithF/doMIsaul |
Package repository | View on CRAN |
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