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

Open Access
Authors
  • S.K. Schaupp
  • F.J. Theis
  • T.G. Schulze
  • N.S. Müller
  • U. Heilbronner
  • R. Batra
  • J. Knauer-Arloth
Publication date 04-2024
Journal Bioinformatics
Article number btae137
Volume | Issue number 40 | 4
Number of pages 4
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
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.
Document type Article
Language English
Published at https://doi.org/10.1093/bioinformatics/btae137
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longmixr (Final published version)
Supplementary materials
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