{
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  "Package": "ipd",
  "Title": "Inference on Predicted Data",
  "Version": "0.4.1.9000",
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  "Description": "Performs valid statistical inference on predicted data\n(IPD) using recent methods, where for a subset of the data, the\noutcomes have been predicted by an algorithm. Provides a\nwrapper function with specified defaults for the type of model\nand method to be used for estimation and inference. Further\nprovides methods for tidying and summarizing results. Salerno\net al., (2025) <doi:10.1093/bioinformatics/btaf055>.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/ipd-tools/ipd, https://ipd-tools.github.io/ipd/",
  "BugReports": "https://github.com/ipd-tools/ipd/issues",
  "VignetteBuilder": "knitr",
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  "Repository": "https://ipd-tools.r-universe.dev",
  "Date/Publication": "2026-03-11 18:02:41 UTC",
  "RemoteUrl": "https://github.com/ipd-tools/ipd",
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  "NeedsCompilation": "no",
  "Packaged": {
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    "User": "root"
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  "Author": "Stephen Salerno [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0003-2763-0494>),\nJiacheng Miao [aut],\nAwan Afiaz [aut],\nKentaro Hoffman [aut],\nJesse Gronsbell [aut],\nJianhui Gao [aut],\nDavid Cheng [aut],\nAnna Neufeld [aut],\nQiongshi Lu [aut],\nTyler H McCormick [aut],\nJeffrey T Leek [aut]",
  "Maintainer": "Stephen Salerno <ssalerno@fredhutch.org>",
  "MD5sum": "2c22f7cb68031fc0a18f22627a7b7dc3",
  "_user": "ipd-tools",
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  "_created": "2026-06-09T06:49:33.000Z",
  "_published": "2026-06-09T07:12:30.748Z",
  "_distro": "noble",
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      "name": "v0.4.1",
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    "augment",
    "chen_logistic",
    "chen_ols",
    "chen_poisson",
    "glance",
    "ipd",
    "pdc_logistic",
    "pdc_ols",
    "pdc_poisson",
    "postpi_analytic_ols",
    "postpi_boot_logistic",
    "postpi_boot_ols",
    "ppi_a_ols",
    "ppi_logistic",
    "ppi_mean",
    "ppi_ols",
    "ppi_plusplus_logistic",
    "ppi_plusplus_logistic_est",
    "ppi_plusplus_mean",
    "ppi_plusplus_mean_est",
    "ppi_plusplus_ols",
    "ppi_plusplus_ols_est",
    "ppi_plusplus_quantile",
    "ppi_plusplus_quantile_est",
    "ppi_quantile",
    "pspa_logistic",
    "pspa_mean",
    "pspa_ols",
    "pspa_poisson",
    "pspa_quantile",
    "show",
    "simdat",
    "tidy"
  ],
  "_help": [
    {
      "page": "A",
      "title": "Calculation of the matrix A based on single dataset",
      "topics": [
        "A"
      ]
    },
    {
      "page": "augment.ipd",
      "title": "Augment data from an ipd fit",
      "topics": [
        "augment.ipd"
      ]
    },
    {
      "page": "calc_lhat_glm",
      "title": "Estimate PPI++ Power Tuning Parameter",
      "topics": [
        "calc_lhat_glm"
      ]
    },
    {
      "page": "chen_logistic",
      "title": "Chen & Chen Logistic",
      "topics": [
        "chen_logistic"
      ]
    },
    {
      "page": "chen_ols",
      "title": "Chen & Chen OLS",
      "topics": [
        "chen_ols"
      ]
    },
    {
      "page": "chen_poisson",
      "title": "Chen & Chen Poisson",
      "topics": [
        "chen_poisson"
      ]
    },
    {
      "page": "compute_cdf",
      "title": "Empirical CDF of the Data",
      "topics": [
        "compute_cdf"
      ]
    },
    {
      "page": "compute_cdf_diff",
      "title": "Empirical CDF Difference",
      "topics": [
        "compute_cdf_diff"
      ]
    },
    {
      "page": "est_ini",
      "title": "Initial estimation",
      "topics": [
        "est_ini"
      ]
    },
    {
      "page": "glance.ipd",
      "title": "Glance at an ipd fit",
      "topics": [
        "glance.ipd"
      ]
    },
    {
      "page": "ipd",
      "title": "Inference on Predicted Data (ipd)",
      "topics": [
        "ipd"
      ]
    },
    {
      "page": "ipd-class",
      "title": "ipd: S4 class for inference on predicted data results",
      "topics": [
        "ipd-class"
      ]
    },
    {
      "page": "link_grad",
      "title": "Gradient of the link function",
      "topics": [
        "link_grad"
      ]
    },
    {
      "page": "link_Hessian",
      "title": "Hessians of the link function",
      "topics": [
        "link_Hessian"
      ]
    },
    {
      "page": "log1pexp",
      "title": "Log1p Exponential",
      "topics": [
        "log1pexp"
      ]
    },
    {
      "page": "logistic_get_stats",
      "title": "Logistic Regression Gradient and Hessian",
      "topics": [
        "logistic_get_stats"
      ]
    },
    {
      "page": "mean_psi",
      "title": "Sample expectation of psi",
      "topics": [
        "mean_psi"
      ]
    },
    {
      "page": "mean_psi_pop",
      "title": "Sample expectation of PSPA psi",
      "topics": [
        "mean_psi_pop"
      ]
    },
    {
      "page": "ols",
      "title": "Ordinary Least Squares",
      "topics": [
        "ols"
      ]
    },
    {
      "page": "ols_get_stats",
      "title": "OLS Gradient and Hessian",
      "topics": [
        "ols_get_stats"
      ]
    },
    {
      "page": "optim_est",
      "title": "One-step update for obtaining estimator",
      "topics": [
        "optim_est"
      ]
    },
    {
      "page": "optim_weights",
      "title": "One-step update for obtaining the weight vector",
      "topics": [
        "optim_weights"
      ]
    },
    {
      "page": "pdc_logistic",
      "title": "PDC Logistic",
      "topics": [
        "pdc_logistic"
      ]
    },
    {
      "page": "pdc_ols",
      "title": "PDC OLS",
      "topics": [
        "pdc_ols"
      ]
    },
    {
      "page": "pdc_poisson",
      "title": "PDC Poisson",
      "topics": [
        "pdc_poisson"
      ]
    },
    {
      "page": "postpi_analytic_ols",
      "title": "PostPI OLS (Analytic Correction)",
      "topics": [
        "postpi_analytic_ols"
      ]
    },
    {
      "page": "postpi_boot_logistic",
      "title": "PostPI Logistic Regression (Bootstrap Correction)",
      "topics": [
        "postpi_boot_logistic"
      ]
    },
    {
      "page": "postpi_boot_ols",
      "title": "PostPI OLS (Bootstrap Correction)",
      "topics": [
        "postpi_boot_ols"
      ]
    },
    {
      "page": "ppi_a_ols",
      "title": "PPI \"All\" OLS",
      "topics": [
        "ppi_a_ols"
      ]
    },
    {
      "page": "ppi_logistic",
      "title": "PPI Logistic Regression",
      "topics": [
        "ppi_logistic"
      ]
    },
    {
      "page": "ppi_mean",
      "title": "PPI Mean Estimation",
      "topics": [
        "ppi_mean"
      ]
    },
    {
      "page": "ppi_ols",
      "title": "PPI OLS",
      "topics": [
        "ppi_ols"
      ]
    },
    {
      "page": "ppi_plusplus_logistic",
      "title": "PPI++ Logistic Regression",
      "topics": [
        "ppi_plusplus_logistic"
      ]
    },
    {
      "page": "ppi_plusplus_logistic_est",
      "title": "PPI++ Logistic Regression (Point Estimate)",
      "topics": [
        "ppi_plusplus_logistic_est"
      ]
    },
    {
      "page": "ppi_plusplus_mean",
      "title": "PPI++ Mean Estimation",
      "topics": [
        "ppi_plusplus_mean"
      ]
    },
    {
      "page": "ppi_plusplus_mean_est",
      "title": "PPI++ Mean Estimation (Point Estimate)",
      "topics": [
        "ppi_plusplus_mean_est"
      ]
    },
    {
      "page": "ppi_plusplus_ols",
      "title": "PPI++ OLS",
      "topics": [
        "ppi_plusplus_ols"
      ]
    },
    {
      "page": "ppi_plusplus_ols_est",
      "title": "PPI++ OLS (Point Estimate)",
      "topics": [
        "ppi_plusplus_ols_est"
      ]
    },
    {
      "page": "ppi_plusplus_quantile",
      "title": "PPI++ Quantile Estimation",
      "topics": [
        "ppi_plusplus_quantile"
      ]
    },
    {
      "page": "ppi_plusplus_quantile_est",
      "title": "PPI++ Quantile Estimation (Point Estimate)",
      "topics": [
        "ppi_plusplus_quantile_est"
      ]
    },
    {
      "page": "ppi_quantile",
      "title": "PPI Quantile Estimation",
      "topics": [
        "ppi_quantile"
      ]
    },
    {
      "page": "print.ipd",
      "title": "Print ipd fit",
      "topics": [
        "print.ipd"
      ]
    },
    {
      "page": "print.summary.ipd",
      "title": "Print summary.ipd",
      "topics": [
        "print.summary.ipd"
      ]
    },
    {
      "page": "psi",
      "title": "Estimating equation",
      "topics": [
        "psi"
      ]
    },
    {
      "page": "pspa_logistic",
      "title": "PSPA Logistic Regression",
      "topics": [
        "pspa_logistic"
      ]
    },
    {
      "page": "pspa_mean",
      "title": "PSPA Mean Estimation",
      "topics": [
        "pspa_mean"
      ]
    },
    {
      "page": "pspa_ols",
      "title": "PSPA OLS Estimation",
      "topics": [
        "pspa_ols"
      ]
    },
    {
      "page": "pspa_poisson",
      "title": "PSPA Poisson Regression",
      "topics": [
        "pspa_poisson"
      ]
    },
    {
      "page": "pspa_quantile",
      "title": "PSPA Quantile Estimation",
      "topics": [
        "pspa_quantile"
      ]
    },
    {
      "page": "pspa_y",
      "title": "PSPA M-Estimation for ML-predicted labels",
      "topics": [
        "pspa_y"
      ]
    },
    {
      "page": "rectified_cdf",
      "title": "Rectified CDF",
      "topics": [
        "rectified_cdf"
      ]
    },
    {
      "page": "rectified_p_value",
      "title": "Rectified P-Value",
      "topics": [
        "rectified_p_value"
      ]
    },
    {
      "page": "show-ipd-method",
      "title": "Show an ipd object",
      "topics": [
        "show,ipd-method"
      ]
    },
    {
      "page": "Sigma_cal",
      "title": "Variance-covariance matrix of the estimation equation",
      "topics": [
        "Sigma_cal"
      ]
    },
    {
      "page": "sim_data_y",
      "title": "Simulate the data for testing the functions",
      "topics": [
        "sim_data_y"
      ]
    },
    {
      "page": "simdat",
      "title": "Data generation function for various underlying models",
      "topics": [
        "simdat"
      ]
    },
    {
      "page": "summary.ipd",
      "title": "Summarize ipd fit",
      "topics": [
        "summary.ipd"
      ]
    },
    {
      "page": "tidy.ipd",
      "title": "Tidy an ipd fit",
      "topics": [
        "tidy.ipd"
      ]
    },
    {
      "page": "wls",
      "title": "Weighted Least Squares",
      "topics": [
        "wls"
      ]
    },
    {
      "page": "zconfint_generic",
      "title": "Normal Confidence Intervals",
      "topics": [
        "zconfint_generic"
      ]
    },
    {
      "page": "zstat_generic",
      "title": "Compute Z-Statistic and P-Value",
      "topics": [
        "zstat_generic"
      ]
    }
  ],
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      "title": "Getting Started with the ipd Package",
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      "headings": [
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        "Background",
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        "Installation",
        "Usage",
        "Data Generation",
        "Function Arguments",
        "Generating Data for Linear Regression",
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        "'Naive' Regression Using the Predicted Outcomes",
        "'Classic' Regression Using only the Labeled Data",
        "Chen and Chen Correction (Gronsbell et al., 2025)",
        "Prediction Decorrelated Inference (Gan et al., 2024)",
        "PostPI Bootstrap Correction (Wang et al., 2020)",
        "PostPI Analytic Correction (Wang et al., 2020)",
        "Prediction-Powered Inference (PPI; Angelopoulos et al., 2023)",
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        "PPI++ (Angelopoulos et al., 2023)",
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        "Printing, Summarizing, and Tidying",
        "Print Method",
        "Summary Method",
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        "Glance Method",
        "Augment Method",
        "Conclusions",
        "Feedback",
        "Contributing",
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      "modified": "2026-03-06 07:12:02",
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