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  "Title": "Design-Adjusted Inference for Pathogen Lineage Surveillance",
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  "Description": "Provides tools for optimizing sequencing resource\nallocation and estimating pathogen lineage prevalence under\nreal-world genomic surveillance conditions. Implements\nconstrained allocation optimization for limited sequencing\ncapacity across multiple regions and sample sources. Includes\nHorvitz-Thompson and post-stratified estimators that account\nfor unequal sequencing rates, delay-adjusted nowcasting for\nright-censored reporting data, and combined design-weighted\ndelay-corrected inference with uncertainty propagation.",
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    "surv_prevalence_by",
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    "surv_reporting_probability",
    "surv_required_sequences",
    "surv_sensitivity",
    "surv_set_weights",
    "surv_simulate",
    "surv_table",
    "surv_update_rates",
    "theme_survinger",
    "tidy"
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      "name": "sarscov2_surveillance",
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      "object": "sarscov2_surveillance",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
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    {
      "page": "glance.surv",
      "title": "One-row summary of survinger model",
      "topics": [
        "glance.surv",
        "glance.surv_adjusted",
        "glance.surv_delay_fit",
        "glance.surv_prevalence"
      ]
    },
    {
      "page": "plot.surv",
      "title": "Plot methods for survinger objects",
      "topics": [
        "plot.surv",
        "plot.surv_adjusted",
        "plot.surv_allocation",
        "plot.surv_delay_fit",
        "plot.surv_design",
        "plot.surv_nowcast",
        "plot.surv_prevalence"
      ]
    },
    {
      "page": "surv_adjusted_prevalence",
      "title": "Combined design-weighted and delay-adjusted prevalence",
      "topics": [
        "as.data.frame.surv_adjusted",
        "print.surv_adjusted",
        "surv_adjusted_prevalence"
      ]
    },
    {
      "page": "surv_optimize_allocation",
      "title": "Optimize sequencing allocation across strata",
      "topics": [
        "as.data.frame.surv_allocation",
        "print.surv_allocation",
        "surv_optimize_allocation"
      ]
    },
    {
      "page": "surv_estimate_delay",
      "title": "Estimate reporting delay distribution",
      "topics": [
        "print.surv_delay_fit",
        "surv_estimate_delay"
      ]
    },
    {
      "page": "surv_design",
      "title": "Create a genomic surveillance design object",
      "topics": [
        "print.summary.surv_design",
        "print.surv_design",
        "summary.surv_design",
        "surv_design"
      ]
    },
    {
      "page": "surv_nowcast_lineage",
      "title": "Nowcast lineage counts correcting for reporting delays",
      "topics": [
        "as.data.frame.surv_nowcast",
        "print.surv_nowcast",
        "surv_nowcast_lineage"
      ]
    },
    {
      "page": "surv_lineage_prevalence",
      "title": "Estimate lineage prevalence with design weights",
      "topics": [
        "as.data.frame.surv_prevalence",
        "print.surv_prevalence",
        "surv_lineage_prevalence"
      ]
    },
    {
      "page": "sarscov2_surveillance",
      "title": "Example SARS-CoV-2 genomic surveillance data",
      "topics": [
        "sarscov2_surveillance"
      ]
    },
    {
      "page": "surv_bind",
      "title": "Combine multiple prevalence estimates",
      "topics": [
        "surv_bind"
      ]
    },
    {
      "page": "surv_compare_allocations",
      "title": "Compare multiple allocation strategies",
      "topics": [
        "surv_compare_allocations"
      ]
    },
    {
      "page": "surv_compare_estimates",
      "title": "Compare weighted vs naive prevalence estimates",
      "topics": [
        "surv_compare_estimates"
      ]
    },
    {
      "page": "surv_design_effect",
      "title": "Compute design effect over time",
      "topics": [
        "surv_design_effect"
      ]
    },
    {
      "page": "surv_detection_probability",
      "title": "Variant detection probability under current design",
      "topics": [
        "surv_detection_probability"
      ]
    },
    {
      "page": "surv_estimate",
      "title": "Pipe-friendly surveillance analysis",
      "topics": [
        "surv_estimate"
      ]
    },
    {
      "page": "surv_filter",
      "title": "Subset a surveillance design by filter criteria",
      "topics": [
        "surv_filter"
      ]
    },
    {
      "page": "surv_naive_prevalence",
      "title": "Compute naive (unweighted) lineage prevalence",
      "topics": [
        "surv_naive_prevalence"
      ]
    },
    {
      "page": "surv_plot_allocation",
      "title": "Plot allocation plan",
      "topics": [
        "surv_plot_allocation"
      ]
    },
    {
      "page": "surv_plot_sequencing_rates",
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      "topics": [
        "surv_plot_sequencing_rates"
      ]
    },
    {
      "page": "surv_power_curve",
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        "plot.surv_power_curve",
        "surv_power_curve"
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    },
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      "page": "surv_prevalence_by",
      "title": "Estimate prevalence by subgroup",
      "topics": [
        "surv_prevalence_by"
      ]
    },
    {
      "page": "surv_quality",
      "title": "Compute surveillance quality metrics",
      "topics": [
        "surv_quality"
      ]
    },
    {
      "page": "surv_report",
      "title": "Generate a comprehensive surveillance system report",
      "topics": [
        "surv_report"
      ]
    },
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      "page": "surv_reporting_probability",
      "title": "Compute cumulative reporting probability",
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      "page": "surv_required_sequences",
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      "title": "Extract tidy estimates from survinger objects",
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