{ "_id": "6a1569f8acfb0bcc41d62cb3", "Package": "r2mlm", "Type": "Package", "Title": "R-Squared Measures for Multilevel Models", "Version": "0.3.8", "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 ", "Description": "Generates both total- and level-specific R-squared\nmeasures from Rights and Sterba’s (2019)\n 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). Bootstrapped confidence\nintervals can also be calculated. 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