AI Article Synopsis

  • Accurate clustering of mixed data types (binary, categorical, continuous) is crucial for effective patient stratification in clinical studies.
  • The longmixr R package offers a robust framework for clustering mixed longitudinal data using advanced finite mixture modeling techniques and includes consensus clustering for stable results.
  • The package is freely available online with comprehensive documentation and a case vignette for easy exploration and visualization of clusters.

Article Abstract

Summary: 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.

Availability And Implementation: The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10994717PMC
http://dx.doi.org/10.1093/bioinformatics/btae137DOI Listing

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