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  "Title": "Model Based Treatment of Missing Data",
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  "Date": "2024-07-15 23:06:34",
  "Author": "Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]",
  "Maintainer": "Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>",
  "Description": "Contains model-based treatment of missing data for\nregression models with missing values in covariates or the\ndependent variable using maximum likelihood or Bayesian\nestimation (Ibrahim et al., 2005;\n<doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West,\n2020a, 2020b;\n<doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>).\nThe regression model can be nonlinear (e.g., interaction\neffects, quadratic effects or B-spline functions). Multilevel\nmodels with missing data in predictors are available for\nBayesian estimation. Substantive-model compatible multiple\nimputation can be also conducted.",
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  "License": "GPL (>= 2)",
  "Config/pak/sysreqs": "cmake make libicu-dev libx11-dev zlib1g-dev",
  "Repository": "https://alexanderrobitzsch.r-universe.dev",
  "Date/Publication": "2024-07-16 07:20:16 UTC",
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