{
  "_id": "6a169f72acfb0bcc41d80276",
  "Package": "NPBayesImputeCat",
  "Type": "Package",
  "Title": "Non-Parametric Bayesian Multiple Imputation for Categorical Data",
  "Version": "0.7",
  "Date": "2026-3-26",
  "Authors@R": "c(person(given = \"Quanli\",\nfamily = \"Wang\",\nrole = \"aut\"),\nperson(given = \"Daniel\",\nfamily = \"Manrique-Vallier\",\nrole = \"aut\"),\nperson(given = c(\"Jerome\", \"P.\"),\nfamily = \"Reiter\",\nrole = \"aut\"),\nperson(given = \"Jingchen\",\nfamily = \"Hu\",\nrole = c(\"aut\", \"cre\"),\nemail = \"jingchen.monika.hu@gmail.com\"))",
  "Maintainer": "Jingchen Hu <jingchen.monika.hu@gmail.com>",
  "Description": "These routines create multiple imputations of missing at\nrandom categorical data, and create multiply imputed synthesis\nof categorical data, with or without structural zeros.\nImputations and syntheses are based on Dirichlet process\nmixtures of multinomial distributions, which is a\nnon-parametric Bayesian modeling approach that allows for\nflexible joint modeling, described in Manrique-Vallier and\nReiter (2014) <doi:10.1080/10618600.2013.844700>.",
  "License": "GPL (>= 3)",
  "RcppModules": "clcm",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-27 07:34:46 UTC",
    "User": "root"
  },
  "Author": "Quanli Wang [aut], Daniel Manrique-Vallier [aut], Jerome P.\nReiter [aut], Jingchen Hu [aut, cre]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-03-28 07:57:24 UTC",
  "RemoteUrl": "https://github.com/cran/NPBayesImputeCat",
  "RemoteRef": "HEAD",
  "RemoteSha": "b39a4bf71708099b6d35695fe648b341e6259f5f",
  "MD5sum": "7d4065258bd3e76867552a561ad80b6c",
  "_user": "cran",
  "_type": "src",
  "_file": "NPBayesImputeCat_0.7.tar.gz",
  "_fileid": "96d2e3d0494c00655eeca27ea9d9ea772c7499648b6365f5a462d9634061a26a",
  "_filesize": 459104,
  "_sha256": "96d2e3d0494c00655eeca27ea9d9ea772c7499648b6365f5a462d9634061a26a",
  "_created": "2026-05-27T07:34:46.000Z",
  "_published": "2026-05-27T07:38:26.155Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 78029466724,
      "time": 165,
      "config": "linux-devel-arm64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7234579642"
    },
    {
      "job": 78029466720,
      "time": 150,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7234574812"
    },
    {
      "job": 78029466709,
      "time": 164,
      "config": "linux-release-arm64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7234579385"
    },
    {
      "job": 78029466704,
      "time": 150,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7234574814"
    },
    {
      "job": 78029034838,
      "time": 173,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7234525626"
    },
    {
      "job": 78029466630,
      "time": 116,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7234563431"
    }
  ],
  "_buildurl": "https://github.com/r-universe/cran/actions/runs/26497424500",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/cran/NPBayesImputeCat",
  "_commit": {
    "id": "b39a4bf71708099b6d35695fe648b341e6259f5f",
    "author": "Jingchen Hu <jingchen.monika.hu@gmail.com>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 0.7\n",
    "time": 1774684644
  },
  "_maintainer": {
    "name": "Jingchen Hu",
    "email": "jingchen.monika.hu@gmail.com"
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "Rcpp",
      "role": "LinkingTo"
    },
    {
      "package": "methods",
      "role": "Imports"
    },
    {
      "package": "rlang",
      "role": "Imports"
    },
    {
      "package": "reshape2",
      "role": "Imports"
    },
    {
      "package": "ggplot2",
      "role": "Imports"
    },
    {
      "package": "dplyr",
      "role": "Imports"
    },
    {
      "package": "bayesplot",
      "role": "Imports"
    },
    {
      "package": "coda",
      "role": "Imports"
    },
    {
      "package": "Rcpp",
      "role": "Imports"
    }
  ],
  "_owner": "cran",
  "_selfowned": false,
  "_usedby": 1,
  "_updates": [
    {
      "week": "2025-49",
      "n": 1
    },
    {
      "week": "2026-13",
      "n": 1
    }
  ],
  "_tags": [
    {
      "name": "0.6",
      "date": "2025-12-01"
    },
    {
      "name": "0.7",
      "date": "2026-03-28"
    }
  ],
  "_stars": 0,
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 615,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/NPBayesImputeCat"
  },
  "_searchresults": 41,
  "_topics": [
    "cpp"
  ],
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NPBayesImputeCat.html",
    "manual.pdf"
  ],
  "_realowner": "cran",
  "_cranurl": false,
  "_releases": [
    {
      "version": "0.1",
      "date": "2018-11-18"
    },
    {
      "version": "0.2",
      "date": "2019-11-09"
    },
    {
      "version": "0.3",
      "date": "2021-01-15"
    },
    {
      "version": "0.4",
      "date": "2021-07-08"
    },
    {
      "version": "0.5",
      "date": "2022-10-03"
    },
    {
      "version": "0.6",
      "date": "2025-12-01"
    },
    {
      "version": "0.7",
      "date": "2026-03-28"
    }
  ],
  "_exports": [
    "compute_probs",
    "CreateModel",
    "DPMPM_nozeros_imp",
    "DPMPM_nozeros_syn",
    "DPMPM_zeros_imp",
    "fit_GLMs",
    "GetDataFrame",
    "GetMCZ",
    "kstar_MCMCdiag",
    "Lcm",
    "marginal_compare_all_imp",
    "marginal_compare_all_syn",
    "pool_estimated_probs",
    "pool_fitted_GLMs",
    "UpdateX"
  ],
  "_datasets": [
    {
      "name": "MCZ",
      "title": "Example dataframe for structrual zeros based on the NYMockexample dataset.",
      "object": "NYexample",
      "file": "NYexample.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "OWNERSHP",
        "MORTGAGE",
        "AGE",
        "SEX",
        "MARST",
        "RACESING",
        "EDUC",
        "EMPSTAT",
        "DISABWRK",
        "VETSTAT"
      ],
      "rows": 60,
      "table": true,
      "tojson": true
    },
    {
      "name": "MCZ",
      "title": "Example dataframe for structrual zeros based on the NYMockexample dataset.",
      "object": "NYMockexample",
      "file": "NYMockexample.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "OWNERSHP",
        "MORTGAGE",
        "AGE",
        "SEX",
        "MARST",
        "RACESING",
        "EDUC",
        "EMPSTAT",
        "DISABWRK",
        "VETSTAT"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "ss16pusa_ds_MCZ",
      "title": "Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.",
      "object": "ss16pusa_ds_MCZ",
      "file": "ss16pusa_ds_MCZ.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "AGEP",
        "MAR",
        "SCHL",
        "SEX",
        "WKL"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "ss16pusa_mi_MCZ",
      "title": "Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.",
      "object": "ss16pusa_mi_MCZ",
      "file": "ss16pusa_mi_MCZ.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "AGEP",
        "MAR",
        "SCHL",
        "SEX",
        "WKL"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "ss16pusa_sample_nozeros",
      "title": "Example dataframe for input categorical data without structural zeros (without missing values).",
      "object": "ss16pusa_sample_nozeros",
      "file": "ss16pusa_sample_nozeros.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "MAR",
        "SEX",
        "WKL"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "ss16pusa_sample_nozeros_miss",
      "title": "Example dataframe for input categorical data without structural zeros (with missing values).",
      "object": "ss16pusa_sample_nozeros_miss",
      "file": "ss16pusa_sample_nozeros_miss.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "MAR",
        "SEX",
        "WKL"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "ss16pusa_sample_zeros",
      "title": "Example dataframe for input categorical data with structural zeros (without missing values).",
      "object": "ss16pusa_sample_zeros",
      "file": "ss16pusa_sample_zeros.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "AGEP",
        "MAR",
        "SCHL",
        "SEX",
        "WKL"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "ss16pusa_sample_zeros_miss",
      "title": "Example dataframe for input categorical data with structural zeros (with missing values).",
      "object": "ss16pusa_sample_zeros_miss",
      "file": "ss16pusa_sample_zeros_miss.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "AGEP",
        "MAR",
        "SCHL",
        "SEX",
        "WKL"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "X",
      "title": "Example dataframe for input categorical data with missing values based on the NYMockexample dataset.",
      "object": "NYexample",
      "file": "NYexample.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "OWNERSHP",
        "MORTGAGE",
        "AGE",
        "SEX",
        "MARST",
        "RACESING",
        "EDUC",
        "EMPSTAT",
        "DISABWRK",
        "VETSTAT"
      ],
      "rows": 20000,
      "table": true,
      "tojson": true
    },
    {
      "name": "X",
      "title": "Example dataframe for input categorical data with missing values based on the NYMockexample dataset.",
      "object": "NYMockexample",
      "file": "NYMockexample.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "OWNERSHP",
        "MORTGAGE",
        "AGE",
        "SEX",
        "MARST",
        "RACESING",
        "EDUC",
        "EMPSTAT",
        "DISABWRK",
        "VETSTAT"
      ],
      "rows": 2000,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "NPBayesImputCat-package",
      "title": "Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros",
      "topics": [
        "NPBayesImputeCat-package",
        "NPBayesImputeCat"
      ]
    },
    {
      "page": "compute_probs",
      "title": "Estimating marginal and joint probabilities in imputed or synthetic datasets",
      "topics": [
        "compute_probs"
      ]
    },
    {
      "page": "CreateModel",
      "title": "Create and initialize the Lcm model object",
      "topics": [
        "CreateModel"
      ]
    },
    {
      "page": "DPMPM_nozeros_imp",
      "title": "Use DPMPM models to impute missing data where there are no structural zeros",
      "topics": [
        "DPMPM_nozeros_imp"
      ]
    },
    {
      "page": "DPMPM_nozeros_syn",
      "title": "Use DPMPM models to synthesize data where there are no structural zeros",
      "topics": [
        "DPMPM_nozeros_syn"
      ]
    },
    {
      "page": "DPMPM_zeros_imp",
      "title": "Use DPMPM models to impute missing data where there are no structural zeros",
      "topics": [
        "DPMPM_zeros_imp"
      ]
    },
    {
      "page": "fit_GLMs",
      "title": "Fit GLM models for imputed or synthetic datasets",
      "topics": [
        "fit_GLMs"
      ]
    },
    {
      "page": "GetDataFrame",
      "title": "Convert imputed data to a dataframe, using the same setting from original input data.",
      "topics": [
        "GetDataFrame"
      ]
    },
    {
      "page": "GetMCZ",
      "title": "Convert disjointed structrual zeros to a dataframe, using the same setting from original structrual zero data.",
      "topics": [
        "GetMCZ"
      ]
    },
    {
      "page": "kstar_MCMCdiag",
      "title": "Perform MCMC diagnostics for kstar",
      "topics": [
        "kstar_MCMCdiag"
      ]
    },
    {
      "page": "Lcm",
      "title": "Class '\"Rcpp_Lcm\"'",
      "topics": [
        "Lcm"
      ]
    },
    {
      "page": "marginal_compare_all_imp",
      "title": "Plot estimated marginal probabilities from observed data vs imputed datasets",
      "topics": [
        "marginal_compare_all_imp"
      ]
    },
    {
      "page": "marginal_compare_all_syn",
      "title": "Plot estimated marginal probabilities from observed data vs synthetic datasets",
      "topics": [
        "marginal_compare_all_syn"
      ]
    },
    {
      "page": "MCZ",
      "title": "Example dataframe for structrual zeros based on the NYMockexample dataset.",
      "topics": [
        "MCZ"
      ]
    },
    {
      "page": "pool_estimated_probs",
      "title": "Pool probability estimates from imputed or synthetic datasets",
      "topics": [
        "pool_estimated_probs"
      ]
    },
    {
      "page": "pool_fitted_GLMs",
      "title": "Pool estimates of fitted GLM models in imputed or synthetic datasets",
      "topics": [
        "pool_fitted_GLMs"
      ]
    },
    {
      "page": "Rcpp_Lcm-class",
      "title": "Rcpp implemenation of the Lcm functions",
      "topics": [
        "Rcpp_Lcm-class"
      ]
    },
    {
      "page": "ss16pusa_ds_MCZ",
      "title": "Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.",
      "topics": [
        "ss16pusa_ds_MCZ"
      ]
    },
    {
      "page": "ss16pusa_mi_MCZ",
      "title": "Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.",
      "topics": [
        "ss16pusa_mi_MCZ"
      ]
    },
    {
      "page": "ss16pusa_sample_nozeros",
      "title": "Example dataframe for input categorical data without structural zeros (without missing values).",
      "topics": [
        "ss16pusa_sample_nozeros"
      ]
    },
    {
      "page": "ss16pusa_sample_nozeros_miss",
      "title": "Example dataframe for input categorical data without structural zeros (with missing values).",
      "topics": [
        "ss16pusa_sample_nozeros_miss"
      ]
    },
    {
      "page": "ss16pusa_sample_zeros",
      "title": "Example dataframe for input categorical data with structural zeros (without missing values).",
      "topics": [
        "ss16pusa_sample_zeros"
      ]
    },
    {
      "page": "ss16pusa_sample_zeros_miss",
      "title": "Example dataframe for input categorical data with structural zeros (with missing values).",
      "topics": [
        "ss16pusa_sample_zeros_miss"
      ]
    },
    {
      "page": "UpdateX",
      "title": "Allow user to update the model with data matrix of same kind.",
      "topics": [
        "UpdateX"
      ]
    },
    {
      "page": "X",
      "title": "Example dataframe for input categorical data with missing values based on the NYMockexample dataset.",
      "topics": [
        "X"
      ]
    }
  ],
  "_rundeps": [
    "abind",
    "backports",
    "bayesplot",
    "checkmate",
    "cli",
    "coda",
    "cpp11",
    "distributional",
    "dplyr",
    "farver",
    "generics",
    "ggplot2",
    "ggridges",
    "glue",
    "gtable",
    "isoband",
    "labeling",
    "lattice",
    "lifecycle",
    "magrittr",
    "matrixStats",
    "numDeriv",
    "pillar",
    "pkgconfig",
    "plyr",
    "posterior",
    "purrr",
    "R6",
    "RColorBrewer",
    "Rcpp",
    "reshape2",
    "rlang",
    "S7",
    "scales",
    "stringi",
    "stringr",
    "tensorA",
    "tibble",
    "tidyr",
    "tidyselect",
    "utf8",
    "vctrs",
    "viridisLite",
    "withr"
  ],
  "_sysdeps": [
    {
      "shlib": "libstdc++",
      "package": "libstdc++6",
      "source": "gcc",
      "version": "14.2.0-4ubuntu2~24.04.1",
      "name": "c++",
      "homepage": "http://gcc.gnu.org/",
      "description": "GNU Standard C++ Library v3"
    }
  ],
  "_score": 2.3909351071033793,
  "_indexed": true,
  "_nocasepkg": "npbayesimputecat",
  "_universes": [
    "cran"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.7",
      "date": "2026-05-27T07:37:21.000Z",
      "distro": "noble",
      "arch": "aarch64",
      "commit": "b39a4bf71708099b6d35695fe648b341e6259f5f",
      "fileid": "9b7708195bae8efe2351bc75e80a5567dcfd0ae535f8e7577b91457513e98889",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26497424500"
    },
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.7",
      "date": "2026-05-27T07:37:02.000Z",
      "distro": "noble",
      "arch": "x86_64",
      "commit": "b39a4bf71708099b6d35695fe648b341e6259f5f",
      "fileid": "b60e95386685da7217ca6943412f9b9f420f095c8273cc9906ea10725b13af27",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26497424500"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "0.7",
      "date": "2026-05-27T07:37:24.000Z",
      "distro": "noble",
      "arch": "aarch64",
      "commit": "b39a4bf71708099b6d35695fe648b341e6259f5f",
      "fileid": "10ce3126ea6f097012328efc3c56a408f070a0a36c5632ebd34f81acb2724df1",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26497424500"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "0.7",
      "date": "2026-05-27T07:37:03.000Z",
      "distro": "noble",
      "arch": "x86_64",
      "commit": "b39a4bf71708099b6d35695fe648b341e6259f5f",
      "fileid": "cae10fef7b048f78911d8a628e18f61d17e65380c36bc4b4e73f0a1abd721420",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26497424500"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "0.7",
      "date": "2026-05-27T07:37:02.000Z",
      "arch": "emscripten",
      "commit": "b39a4bf71708099b6d35695fe648b341e6259f5f",
      "fileid": "43c8c0ff60562e3d17dd765435289d7572aa7653070c03fc3dc059ba3117d74a",
      "status": "success",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26497424500"
    }
  ]
}