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  "Title": "R-Squared Measures for Multilevel Models",
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  "Authors@R": "c(\nperson(\"Mairead\", \"Shaw\", email = \"mairead.shaw@mail.mcgill.ca\", role = c(\"aut\", \"cre\")),\nperson(\"Jason\", \"Rights\", email = \"jrights@psych.ubc.ca\", role = \"aut\"),\nperson(\"Sonya\", \"Sterba\", email = \"sonya.sterba@vanderbilt.edu\", role = \"aut\"),\nperson(\"Jessica\", \"Flake\", email = \"jessica.flake@mcgill.ca\", role = \"aut\"))",
  "Maintainer": "Mairead Shaw <mairead.shaw@mail.mcgill.ca>",
  "Description": "Generates both total- and level-specific R-squared\nmeasures from Rights and Sterba’s (2019)\n<doi:10.1037/met0000184> framework of R-squared measures for\nmultilevel models with random intercepts and/or slopes, which\nis based on a complete decomposition of variance. Additionally\ngenerates graphical representations of these R-squared measures\nto allow visualizing and interpreting all measures in the\nframework together as an integrated set. This framework\nsubsumes 10 previously-developed R-squared measures for\nmultilevel models as special cases of 5 measures from the\nframework, and it also includes several newly-developed\nmeasures. Measures in the framework can be used to compute\nR-squared differences when comparing multilevel models\n(following procedures in Rights & Sterba (2020)\n<doi:10.1080/00273171.2019.1660605>). Bootstrapped confidence\nintervals can also be calculated. To use the confidence\ninterval functionality, download bootmlm from\n<https://github.com/marklhc/bootmlm>.",
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