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  "Title": "Cluster High Dimensional Categorical Datasets",
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  "Description": "Scalable Bayesian clustering of categorical datasets. The\npackage implements a hierarchical Dirichlet (Process) mixture\nof multinomial distributions. It is thus a probabilistic latent\nclass model (LCM) and can be used to reduce the dimensionality\nof hierarchical data and cluster individuals into latent\nclasses. It can automatically infer an appropriate number of\nlatent classes or find k classes, as defined by the user.  The\nmodel is based on a paper by Dunson and Xing (2009)\n<doi:10.1198/jasa.2009.tm08439>, but implements a scalable\nvariational inference algorithm so that it is applicable to\nlarge datasets. It is described and tested in the accompanying\npaper by Ahlmann-Eltze and Yau (2018)\n<doi:10.1109/DSAA.2018.00068>.",
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  "Repository": "https://const-ae.r-universe.dev",
  "Date/Publication": "2023-09-11 18:19:37 UTC",
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