pareto

dataframe or matrix containing the objective values for
each case

bi.objective

name of the two objectives (should be in
<code>colnames(bi.objetive)</code>).

obj.names

Find Pareto optimal and Pareto non optimal solutions

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

pareto: Pareto optimization

Description

Usage

Arguments

Value