{ "_id": "6ad8748c0d12c08e591b9e41", "Package": "MIRES", "Title": "Measurement Invariance Assessment Using Random Effects Models\nand Shrinkage", "Version": "0.1.1", "Authors@R": "c(person(given = \"Stephen\",\nfamily = \"Martin\",\nrole = c(\"aut\", \"cre\"),\nemail = \"stephenSRMMartin@gmail.com\",\ncomment = c(ORCID = \"0000-0001-8085-2390\")),\nperson(given = \"Philippe\",\nfamily = \"Rast\",\nrole = \"aut\",\nemail = \"rast.ph@gmail.com\",\ncomment = c(ORCID = \"0000-0003-3630-6629\")))", "Description": "Estimates random effect latent measurement models, wherein\nthe loadings, residual variances, intercepts, latent means, and\nlatent variances all vary across groups. The random effect\nvariances of the measurement parameters are then modeled using\na hierarchical inclusion model, wherein the inclusion of the\nvariances (i.e., whether it is effectively zero or non-zero) is\ninformed by similar parameters (of the same type, or of the\nsame item). This additional hierarchical structure allows the\nevidence in favor of partial invariance to accumulate more\nquickly, and yields more certain decisions about measurement\ninvariance. 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