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  "Title": "Correcting Misclassified Binary Outcomes in Association Studies",
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  "Description": "Use frequentist and Bayesian methods to estimate\nparameters from a binary outcome misclassification model. These\nmethods correct for the problem of \"label switching\" by\nassuming that the sum of outcome sensitivity and specificity is\nat least 1. A description of the analysis methods is available\nin Hochstedler and Wells (2023)\n<doi:10.48550/arXiv.2303.10215>.",
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      "title": "Observed Data Log-Likelihood Function for Estimation of the Naive Two-Stage Misclassification Model",
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      "title": "EM-Algorithm Estimation of the Binary Outcome Misclassification Model while Assuming Perfect Sensitivity",
      "topics": [
        "perfect_sensitivity_EM"
      ]
    },
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      "page": "pi_compute",
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      "page": "pistar_by_chain",
      "title": "Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects for each MCMC Chain",
      "topics": [
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      "page": "pistar_compute_for_chains",
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      "title": "Compute the Mean Conditional Probability of Second-Stage Correct Classification, by First-Stage and True Outcome Across all Subjects for each MCMC Chain",
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      "title": "Sum Every \"n\"th Element, then add 1",
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      "topics": [
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    },
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      "title": "Synthetic example data of pretrial failure risk factors and outcomes, VPRAI recommendations, and judge decisions",
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      "title": "Compute E-step for Binary Outcome Misclassification Model Estimated With the EM-Algorithm",
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      "page": "w_j_2stage",
      "title": "Compute E-step for Two-Stage Binary Outcome Misclassification Model Estimated With the EM-Algorithm",
      "topics": [
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