{ "_id": "6a48740ee06c18f0a653c22f", "Package": "lcc", "Type": "Package", "Title": "Advanced Analysis of Longitudinal Data Using the Concordance\nCorrelation Coefficient", "Version": "4.2.2", "Date": "2025-11-23", "Authors@R": "c(\nperson(\"Thiago\", \"de Paula Oliveira\", role = c(\"aut\", \"cre\"),\nemail = \"thiago.paula.oliveira@alumni.usp.br\",\ncomment = c(ORCID = \"0000-0002-4555-2584\")),\nperson(\"Rafael\", \"de Andrade Moral\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-0875-3563\")),\nperson(\"Silvio\", \"Sandoval Zocchi\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0003-2535-908X\")),\nperson(\"Clarice\", \"Garcia Borges Demetrio\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-3609-178X\")),\nperson(\"John\", \"Hinde\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-6507-5204\")))", "Description": "Methods for assessing agreement between repeated\nmeasurements obtained by two or more methods using the\nlongitudinal concordance correlation coefficient (LCC).\nPolynomial mixed-effects models (via 'nlme') describe how\nconcordance, Pearson correlation and accuracy evolve over time.\nFunctions are provided for model fitting, diagnostic plots,\nextraction of summaries, and non-parametric bootstrap\nconfidence intervals (including parallel computation),\nfollowing Oliveira et al. 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