longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types

Jonas Hagenberg, Monika Budde, Teodora Pandeva, Ivan Kondofersky, Sabrina K. Schaupp, Fabian J. Theis, Thomas G. Schulze, Nikola S. Muller, Urs Heilbronner, Richa Batra, Janine Knauer-Arloth

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization.

Original languageEnglish
Article numberbtae137
JournalBioinformatics
Volume40
Issue number4
DOIs
StatePublished - 1 Apr 2024

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