{ "_id": "6ae31c68327abcdf86866759", "Package": "LMMELSM", "Title": "Fit Latent Multivariate Mixed Effects Location Scale Models", "Version": "0.2.1", "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": "In addition to modeling the expectation (location) of an\noutcome, mixed effects location scale models (MELSMs) include\nsubmodels on the variance components (scales) directly. This\nallows models on the within-group variance with mixed effects,\nand between-group variances with fixed effects. The MELSM can\nbe used to model volatility, intraindividual variance,\nuncertainty, measurement error variance, and more. Multivariate\nMELSMs (MMELSMs) extend the model to include multiple\ncorrelated outcomes, and therefore multiple locations and\nscales. The latent multivariate MELSM (LMMELSM) further\nincludes multiple correlated latent variables as outcomes. This\npackage implements two-level mixed effects location scale\nmodels on multiple observed or latent outcomes, and\nbetween-group variance modeling. Williams, Martin, Liu, and\nRast (2020) . 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