ZytoHMGU/hetset: Identification of Heterogeneous Subsets in Data

An explorative tool that scans data for feature sets carrying highest degree of heterogeneity. Starting from raw data, a subset of labelled samples or features of interest, a set of features is elaborated, that separates the samples in subpopulations. An unsupervised adaption of the forward subset selection approach known from supervised machine learning settings is used. Hellinger's squared distance replaces goodness of fit criteria.

install.packages("remotes")
remotes::install_github("ZytoHMGU/hetset")