ArticleBioinformatics (Oxford, England)2024
longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Early divergence of treatment response trajectories to adalimumab or its biosimilar in active ankylosing spondylitis: consensus clustering analysis of a randomized controlled trial.Frontiers in immunology · 2026Trial
- Identification of Fiber and Asphalt Mastic Interface Failure Modes Based on the Improved Growth Curve Mixture Model.Materials (Basel, Switzerland) · 2026Article
- Identification of a robust metabolic signature associated with hospital-acquired pneumonia and response to interferon-gamma treatment in critically ill patients.Critical care (London, England) · 2026Observational
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11 authors.
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Abstract
summaryAccurate 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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