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  "Description": "It is very common nowadays for a study to collect multiple\nfeatures and appropriately integrating multiple longitudinal\nfeatures simultaneously for defining individual clusters\nbecomes increasingly crucial to understanding population\nheterogeneity and predicting future outcomes.  'BCClong'\nimplements a Bayesian consensus clustering (BCC) model for\nmultiple longitudinal features via a generalized linear mixed\nmodel. Compared to existing packages, several key features make\nthe 'BCClong' package appealing: (a) it allows simultaneous\nclustering of mixed-type (e.g., continuous, discrete and\ncategorical) longitudinal features, (b) it allows each\nlongitudinal feature to be collected from different sources\nwith measurements taken at distinct sets of time points (known\nas irregularly sampled longitudinal data), (c) it relaxes the\nassumption that all features have the same clustering structure\nby estimating the feature-specific (local) clusterings and\nconsensus (global) clustering.",
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