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  "Package": "isni",
  "Title": "Index of Local Sensitivity to Nonignorability",
  "Version": "1.3",
  "Author": "Hui Xie <huixie@uic.edu>, Weihua Gao, Baodong Xing, Daniel\nHeitjan, Donald Hedeker, Chengbo Yuan",
  "Description": "The current version provides functions to compute, print\nand summarize the Index of Sensitivity to Nonignorability\n(ISNI) in the generalized linear model for independent data,\nand in the marginal multivariate Gaussian model and the\nmixed-effects models for continuous and binary\nlongitudinal/clustered data. It allows for arbitrary patterns\nof missingness in the regression outcomes caused by dropout\nand/or intermittent missingness. One can compute the\nsensitivity index without estimating any nonignorable models or\npositing specific magnitude of nonignorability. Thus ISNI\nprovides a simple quantitative assessment of how robust the\nstandard estimates assuming missing at random is with respect\nto the assumption of ignorability. For a tutorial, download at\n<https://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.\nFor more details, see Troxel Ma and Heitjan (2004) and Xie and\nHeitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel\nand Heitjan (2005) <doi:10.1002/sim.2107> and Xie (2008)\n<doi:10.1002/sim.3117> and Xie (2012)\n<doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012)\n<doi:10.1002/jae.1157>.",
  "License": "GPL-2",
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    "print.isniglmm",
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    "summary.isnilmm",
    "summary.isnimgm",
    "tmdm"
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        "time",
        "sub",
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        "y",
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        "yp",
        "g",
        "gp",
        "basey"
      ],
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        "quit",
        "time",
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        "helmert3"
      ],
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    {
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      "title": "Dataset for a survey of sexual behavior",
      "object": "sos",
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        "data.frame"
      ],
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        "sexact",
        "gender",
        "faculty"
      ],
      "rows": 6136,
      "table": true,
      "tojson": true
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  ],
  "_help": [
    {
      "page": "coc",
      "title": "A data set for Psychiatric Drug Treatment",
      "topics": [
        "coc"
      ]
    },
    {
      "page": "definemissingstatus",
      "title": "Utility function to generate missing status variables in longitudinal data with dropout and/or intermittent missingness.",
      "topics": [
        "definemissingstatus"
      ]
    },
    {
      "page": "isniglm",
      "title": "Function for ISNI computation when the outcome follows GLMs.",
      "topics": [
        "isniglm"
      ]
    },
    {
      "page": "isniglmmbin",
      "title": "Function for ISNI computation when the longitudinal/clustered binary outcome follows a GLMM.",
      "topics": [
        "isniglmmbin"
      ]
    },
    {
      "page": "isnilmm",
      "title": "Function for ISNI computation when the outcome follows LMM.",
      "topics": [
        "isnilmm"
      ]
    },
    {
      "page": "isnimgm",
      "title": "Function for ISNI computation when the outcome follows marginal multivariate Gaussian Models.",
      "topics": [
        "isnimgm"
      ]
    },
    {
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      "title": "Function to print the isniglm object.",
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      ]
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      "title": "Function to print the isniglmm object.",
      "topics": [
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    {
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      "title": "Function to print the isnilmm object.",
      "topics": [
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      "title": "Function to fit the transitional missing data model and obtain the predicted probabilities of being observed for all observations.",
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