{ "_id": "6a1978e9acfb0bcc41df3f38", "Package": "miceadds", "Type": "Package", "Title": "Some Additional Multiple Imputation Functions, Especially for\n'mice'", "Version": "3.21-1", "Date": "2026-05-29 07:56:46", "Authors@R": "c(person(given = \"Alexander\",\nfamily = \"Robitzsch\",\nrole = c(\"aut\", \"cre\"),\nemail = \"robitzsch@ipn.uni-kiel.de\",\ncomment = c(ORCID = \"0000-0002-8226-3132\")),\nperson(given = \"Simon\",\nfamily = \"Grund\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-1290-8986\")),\nperson(given = \"Thorsten\",\nfamily = \"Henke\",\nrole = \"ctb\"))", "Maintainer": "Alexander Robitzsch ", "Description": "Contains functions for multiple imputation which\ncomplements existing functionality in R. In particular, several\nimputation methods for the mice package (van Buuren &\nGroothuis-Oudshoorn, 2011, ) are\nimplemented. Main features of the miceadds package include\nplausible value imputation (Mislevy, 1991,\n), multilevel imputation for variables\nat any level or with any number of hierarchical and\nnon-hierarchical levels (Grund, Luedtke & Robitzsch, 2018,\n; van Buuren, 2018, Ch.7,\n), imputation using partial least\nsquares (PLS) for high dimensional predictors (Robitzsch, Pham\n& Yanagida, 2016), nested multiple imputation (Rubin, 2003,\n), substantive model compatible\nimputation (Bartlett et al., 2015,\n), and features for the\ngeneration of synthetic datasets (Reiter, 2005,\n; Nowok, Raab, & Dibben,\n2016, ).", "URL": "https://github.com/alexanderrobitzsch/miceadds,\nhttps://sites.google.com/view/alexander-robitzsch/software", "License": "GPL (>= 2)", "BugReports": "https://github.com/alexanderrobitzsch/miceadds/issues?state=open", "Config/pak/sysreqs": "cmake make libicu-dev libx11-dev zlib1g-dev", "Repository": "https://alexanderrobitzsch.r-universe.dev", "Date/Publication": "2026-05-29 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