my_jack

dataframe containing the partitions (factors). Specifically, each
column must be a partition and must be a factor.

data

Numeric (in (0, 1)), minimum <code>Jaccard index</code> value
between partitions to keep them (0 = all partitions kept).

mcons.JAC.sel

Evaluates if all partitions are the same (and thus no consensus algorithm
needed) and may pre-select partitions depending on a threshold on the Jaccard
index

internal

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.

Lilith Faucheux

my_jack: Partition sorting based on Jaccard index

Description

Usage

Arguments

Value