future (>= 1.6.2), foreach (>= 1.4.3), iterators (>= 1.0.8),
parallel, utils

Foreach parallel adaptor for the multicore package
Provides a parallel backend for the %dopar% function using
Simon Urbanek's multicore package.

Provides a parallel backend for the %dopar% function using
the multicore functionality of the parallel package..

c(person("Rich", "Calaway", role="cre", email="richcala@microsoft.com"),
person("Revolution", "Analytics", role=c("aut", "cph")),
person("Steve", "Weston", role="aut"))
Provides a parallel backend for the %dopar% function using
the multicore functionality of the parallel package.
Rich Calaway [cre],
Revolution Analytics [aut, cph],
Steve Weston [aut]

c(person("Hong", "Ooi", role="cre", email="hongooi@microsoft.com"),
person("Revolution", "Analytics", role=c("aut", "cph")),
person("Steve", "Weston", role="aut"))
Hong Ooi [cre],
Revolution Analytics [aut, cph],
Steve Weston [aut]

c(person("Michelle", "Wallig", role="cre", email="Michelle.Wallig@microsoft.com"),
person("Revolution", "Analytics", role=c("aut", "cph")),
person("Steve", "Weston", role="aut"))
Michelle Wallig [cre],
Revolution Analytics [aut, cph],
Steve Weston [aut]

c(person("Folashade", "Daniel", role="cre", email="fdaniel@microsoft.com"),
person("Revolution", "Analytics", role=c("aut", "cph")),
person("Steve", "Weston", role="aut"))
Folashade Daniel [cre],
Revolution Analytics [aut, cph],
Steve Weston [aut]

Do Multiple Imputation-Based Semi-Supervised and Unsupervised
Learning
c(person(given = "Lilith",
family = "Faucheux",
role = c("aut", "cre"),
email = "lilith.faucheux@inserm.fr"),
person(given = "Sylvie",
family = "Chevret",
role = "ths"),
person(given = "Matthieu",
family = "Resche-Rigon",
role = "ctb"),
person(given = "Marie",
family = "Perrot-Dockès",
role = "ctb"),
person(given = "Eric",
family = "Han",
role = "ctb"))
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.
aricode, arules, clusterCrit, dplyr, ggplot2, Gmedian,
graphics, MASS, methods, mice, NbClust, ncvreg, plyr, scales,
stats, survival, utils, withr
censReg, cluster, CPE, dbscan, e1071, ggpubr, Hmisc, igraph,
mclust, parallel, RColorBrewer, reshape2, testthat (>= 3.0.0),
timeROC, truncnorm
Lilith Faucheux [aut, cre],
Sylvie Chevret [ths],
Matthieu Resche-Rigon [ctb],
Marie Perrot-Dockès [ctb],
Eric Han [ctb]