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Updated: Jun 30, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data
Jonas Hagenberg1,2,3, Monika Budde4, Teodora Pandeva3,5,6
1Max Planck Institute of Psychiatry, 80804 Munich, Germany.
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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