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  "Title": "Confidence Intervals for Robust and Classical Linear Mixed Model\nEstimators",
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  "Date": "2026-01-28",
  "Authors@R": "c(person(\"Fabio\", \"Mason\", email = \"fabio.mason@unige.ch\", role = c(\"aut\", \"cre\")),\nperson(\"Manuel\", \"Koller\", email = \"kollerma@proton.me\", role = \"aut\"),\nperson(\"Eva\", \"Cantoni\", email = \"Eva.Cantoni@unige.ch\", role = c(\"ctb\", \"ths\")),\nperson(\"Paolo\", \"Ghisletta\", email = \"Paolo.Ghisletta@unige.ch\", role = \"ths\"))",
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  "Description": "The main function calculates confidence intervals (CI) for\nMixed Models, utilizing both classical estimators from the\nlmer() function in the 'lme4' package and robust estimators\nfrom the rlmer() function in the 'robustlmm' package, as well\nas the varComprob() function in the 'robustvarComp' package.\nThree methods are available: the classical Wald method, the\nwild bootstrap, and the parametric bootstrap. Bootstrap methods\noffer flexibility in obtaining lower and upper bounds through\npercentile or BCa methods. More details are given in Mason, F.,\nCantoni, E., & Ghisletta, P. (2021) <doi:10.5964/meth.6607> and\nMason, F., Cantoni, E., & Ghisletta, P. (2024)\n<doi:10.1037/met0000643>.",
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