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  "Title": "Hierarchical Bayesian Small Area Estimation",
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  "Description": "Functions to compute small area estimates based on a basic\narea or unit-level model. The model is fit using restricted\nmaximum likelihood, or in a hierarchical Bayesian way. In the\nlatter case numerical integration is used to average over the\nposterior density for the between-area variance. The output\nincludes the model fit, small area estimates and corresponding\nmean squared errors, as well as some model selection measures.\nAdditional functions provide means to compute aggregate\nestimates and mean squared errors, to minimally adjust the\nsmall area estimates to benchmarks at a higher aggregation\nlevel, and to graphically compare different sets of small area\nestimates.",
  "Date": "2022-03-03",
  "RoxygenNote": "7.1.2",
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  "Author": "Harm Jan Boonstra [aut, cre]",
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