{ "_id": "6a60a197c766cc6deb6a9f1f", "Package": "clustGLMM", "Title": "Model-Based Clustering of Mixed-Type Longitudinal Data", "Version": "1.0", "Encoding": "UTF-8", "Authors@R": "c(person(\"Jan\", \"Vavra\", role = c(\"aut\", \"cre\"),\nemail = \"vavraj@karlin.mff.cuni.cz\",\ncomment = c(ORCID = \"0000-0003-0068-5356\")),\nperson(\"Bettina\", \"Gruen\", role = c(\"aut\"),\nemail = \"bettina.gruen@R-project.org\",\ncomment = c(ORCID = \"0000-0001-7265-4773\")),\nperson(\"Gertraud\", \"Malsiner-Walli\", role = c(\"aut\"),\nemail = \"gertraud.malsiner-walli@wu.ac.at\",\ncomment = c(ORCID = \"0000-0002-1213-4749\")),\nperson(\"Arnost\", \"Komarek\", role = c(\"aut\"),\nemail = \"komarek@karlin.mff.cuni.cz\",\ncomment = c(ORCID = \"0000-0001-8778-3762\")))", "Maintainer": "Jan Vavra ", "Description": "Provides tools for Bayesian estimation and inference for\nmodelling clusterwise multivariate regression models for\nnumeric, count, binary, ordinal and count outcomes observed\nrepeatedly on the same units and where possible relations among\noutcomes are captured through a joint distribution of random\neffects. The clusters are defined through cluster-specific\nparameters, which the analyst can choose, e.g., with respect to\nthe regression coefficients. In particular, the model\nspecification for each regression model via the formula is\nspecific to the outcome and consists of four parts: (1) fixed -\nregression coefficients common to all clusters, (2) group -\ngroup-specific regression coefficients, (3) random - random\neffects specific for each unit, (3) offset - name of an offset\nvariable (if needed). Estimation is performed using MCMC\nsampling combining Gibbs and Metropolis-Hastings steps.\nPost-processing tools allow to assess convergence and address\nlabel switching and provide visual diagnostics. Units may be\nclassified based on sampled allocation indicators or by\nexploiting the posterior distribution of the classification\nprobabilities. For more details see Vavra et al. 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