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.
| Version: |
1.0.1 |
| Imports: |
aricode, arules, clusterCrit, dplyr, ggplot2, Gmedian, graphics, MASS, methods, mice, NbClust, ncvreg, plyr, scales, stats, survival, utils, withr |
| Suggests: |
censReg, cluster, clustMixType, CPE, dbscan, e1071, ggpubr, Hmisc, igraph, mclust, parallel, RColorBrewer, reshape2, testthat (≥ 3.0.0), timeROC, truncnorm |
| Published: |
2021-10-18 |
| Author: |
Lilith Faucheux [aut, cre],
Sylvie Chevret [ths],
Matthieu Resche-Rigon [ctb],
Marie Perrot-Dockès [ctb],
Eric Han [ctb] |
| Maintainer: |
Lilith Faucheux <lilith.faucheux at inserm.fr> |
| BugReports: |
https://github.com/LilithF/doMIsaul/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/LilithF/doMIsaul |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README |
| CRAN checks: |
doMIsaul results |