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  "Package": "RfEmpImp",
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  "Title": "Multiple Imputation using Chained Random Forests",
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  "Authors@R": "c(person(\"Shangzhi\", \"Hong\", role = c(\"aut\", \"cre\"),\nemail = \"shangzhi-hong@hotmail.com\"),\nperson(\"Henry S.\", \"Lynn\", role = c(\"ths\")))",
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  "Description": "An R package for multiple imputation using chained random\nforests. Implemented methods can handle missing data in mixed\ntypes of variables by using prediction-based or node-based\nconditional distributions constructed using random forests. For\nprediction-based imputation, the method based on the empirical\ndistribution of out-of-bag prediction errors of random forests\nand the method based on normality assumption for prediction\nerrors of random forests are provided for imputing continuous\nvariables. And the method based on predicted probabilities is\nprovided for imputing categorical variables. For node-based\nimputation, the method based on the conditional distribution\nformed by the predicting nodes of random forests, and the\nmethod based on proximity measures of random forests are\nprovided. More details of the statistical methods can be found\nin Hong et al. (2020) <arXiv:2004.14823>.",
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  "URL": "https://github.com/shangzhi-hong/RfEmpImp",
  "BugReports": "https://github.com/shangzhi-hong/RfEmpImp/issues",
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  "Repository": "https://shangzhi-hong.r-universe.dev",
  "Date/Publication": "2022-10-20 08:31:45 UTC",
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      "topics": [
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    {
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      "title": "Perform multiple imputation using the empirical error distributions and predicted probabilities of random forests",
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      "page": "mice.impute.rfemp",
      "title": "Univariate sampler function for mixed types of variables for prediction-based imputation, using empirical distribution of out-of-bag prediction errors and predicted probabilities of random forests",
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