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  "Title": "General Linear Mixed Models for Gene-Level Differential\nExpression",
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  "Description": "Using mixed effects models to analyse longitudinal gene\nexpression can highlight differences between sample groups over\ntime. The most widely used differential gene expression tools\nare unable to fit linear mixed effect models, and are less\noptimal for analysing longitudinal data. This package provides\nnegative binomial and Gaussian mixed effects models to fit gene\nexpression and other biological data across repeated samples.\nThis is particularly useful for investigating changes in\nRNA-Sequencing gene expression between groups of individuals\nover time, as described in: Rivellese, F., Surace, A. E.,\nGoldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M.\nJ., & Pitzalis, C. (2022) Nature medicine\n<doi:10.1038/s41591-022-01789-0>.",
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  "Repository": "https://myles-lewis.r-universe.dev",
  "Date/Publication": "2025-10-01 19:55:13 UTC",
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    "User": "root"
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  "Author": "Myles Lewis [aut, cre] (ORCID: <https://orcid.org/0000-0001-9365-5345>),\nKatriona Goldmann [aut] (ORCID:\n<https://orcid.org/0000-0002-9073-6323>),\nElisabetta Sciacca [aut] (ORCID:\n<https://orcid.org/0000-0001-7525-1558>),\nCankut Cubuk [ctb] (ORCID: <https://orcid.org/0000-0003-4646-0849>),\nAnna Surace [ctb] (ORCID: <https://orcid.org/0000-0001-9589-3005>)",
  "Maintainer": "Myles Lewis <myles.lewis@qmul.ac.uk>",
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