doMIsaul: Do Multiple Imputation-Based Semi-Supervised and Unsupervised Learning

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: 1.0.0
Imports: aricode, arules, clusterCrit, dplyr, ggplot2, Gmedian, graphics, MASS, methods, mice, NbClust, ncvreg, plyr, scales, stats, survival, utils, withr
Suggests: censReg, cluster, CPE, dbscan, e1071, ggpubr, Hmisc, igraph, mclust, parallel, RColorBrewer, reshape2, testthat (≥ 3.0.0), timeROC, truncnorm
Published: 2021-08-05
Author: Lilith Faucheux [aut, cre], Sylvie Chevret [ths], Matthieu Resche-Rigon [ctb], Marie Perrot-Dock├Ęs [ctb], Eric Han [ctb]
Maintainer: Lilith Faucheux <lilith.faucheux at>
License: GPL (≥ 3)
NeedsCompilation: no
Language: en-US
Materials: README
CRAN checks: doMIsaul results


Reference manual: doMIsaul.pdf
Package source: doMIsaul_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): doMIsaul_1.0.0.tgz, r-release (x86_64): doMIsaul_1.0.0.tgz, r-oldrel: doMIsaul_1.0.0.tgz


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