{ "_id": "6a2e68f83efcd9bda4311b05", "Package": "synMicrodata", "Type": "Package", "Title": "Synthetic Microdata Generator", "Version": "2.1.3", "Date": "2025-11-26", "Authors@R": "c(person(\"Juhee\", \"Lee\", role = c(\"aut\", \"cre\"),\nemail = \"ljh988488@gmail.com\"),\nperson(\"Hang J.\", \"Kim\", role = \"aut\"),\nperson(\"Young-Min\", \"Kim\", role = \"aut\"),\nperson(\"Jared\", \"Murray\", role = \"aut\"))", "Maintainer": "Juhee Lee ", "Description": "This tool fits a non-parametric Bayesian model called a\n\"hierarchically coupled mixture model with local dependence\n(HCMM-LD)\" to the original microdata in order to generate\nsynthetic microdata for privacy protection. The non-parametric\nfeature of the adopted model is useful for capturing the joint\ndistribution of the original input data in a highly flexible\nmanner, leading to the generation of synthetic data whose\ndistributional features are similar to that of the input data.\nThe package allows the original input data to have missing\nvalues and impute them with the posterior predictive\ndistribution, so no missing values exist in the synthetic data\noutput. The method builds on the work of Murray and Reiter\n(2016) .", "License": "GPL (>= 3)", "RcppModules": "IO_module", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-14 08:36:42 UTC", "User": "root" }, "Author": "Juhee Lee [aut, cre], Hang J. 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