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) and Faucheux et al. (2021) , respectively.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("doMIsaul")

1.0.0 by Lilith Faucheux, 2 months ago


https://github.com/LilithF/doMIsaul


Report a bug at https://github.com/LilithF/doMIsaul/issues


Browse source code at https://github.com/cran/doMIsaul


Authors: Lilith Faucheux [aut, cre] , Sylvie Chevret [ths] , Matthieu Resche-Rigon [ctb] , Marie Perrot-Dock├Ęs [ctb] , Eric Han [ctb]


Documentation:   PDF Manual  


GPL (>= 3) license


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, timeROC, truncnorm


See at CRAN