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  "Description": "Helps create alerts and determine trends by using various\nmethods to analyze public health surveillance data. The primary\nanalysis method is based upon a published analytics strategy by\nBenedetti (2019) <doi:10.5588/pha.19.0002>.",
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    "add_holiday_effect",
    "compare_results",
    "csfmt_ensemble_v3",
    "csfmt_interpret",
    "csfmt_parse",
    "csfmt_reporting_triangle_v3",
    "csfmt_var",
    "ens_add_rate",
    "ens_collapse",
    "mem_thresholds_v1",
    "nowcast_backtest",
    "nowcast_censor",
    "nowcast_evaluate_v1",
    "nowcast_passthrough_to_ensemble_v1",
    "nowcast_quasipoisson_v1",
    "nowcast_truth",
    "q_label",
    "q_value",
    "qc_surveillance_data",
    "qc_week_over_week",
    "reporting_completion",
    "reporting_completion_trend_v1",
    "reporting_triangle_matrix",
    "rolling_slope_matrix",
    "set_time_series_id",
    "short_term_trend",
    "short_term_trend_sts_v1",
    "signal_detection_hlm",
    "simulate_baseline_data",
    "simulate_seasonal_outbreak_data",
    "simulate_spike_outbreak_data",
    "validate_ensemble"
  ],
  "_help": [
    {
      "page": "add_holiday_effect",
      "title": "Apply a public holiday effect to simulated data",
      "topics": [
        "add_holiday_effect"
      ]
    },
    {
      "page": "compare_results",
      "title": "Compare two collapsed csfmt result sets",
      "topics": [
        "compare_results"
      ]
    },
    {
      "page": "csfmt_ensemble_v3",
      "title": "Construct a csfmt_ensemble_v3",
      "topics": [
        "csfmt_ensemble_v3"
      ]
    },
    {
      "page": "csfmt_interpret",
      "title": "Interpret a dataset's columns via the naming grammar",
      "topics": [
        "csfmt_interpret"
      ]
    },
    {
      "page": "csfmt_parse",
      "title": "Parse a csfmt measure column name into components (inverse of [csfmt_var])",
      "topics": [
        "csfmt_parse"
      ]
    },
    {
      "page": "csfmt_reporting_triangle_v3",
      "title": "Construct a csfmt_reporting_triangle_v3",
      "topics": [
        "csfmt_reporting_triangle_v3"
      ]
    },
    {
      "page": "csfmt_var",
      "title": "Construct a csfmt measure column name from components",
      "topics": [
        "csfmt_var"
      ]
    },
    {
      "page": "ens_add_rate",
      "title": "Add a rate measure to an ensemble",
      "topics": [
        "ens_add_rate",
        "ens_add_rate.csfmt_ensemble_v3"
      ]
    },
    {
      "page": "ens_collapse",
      "title": "Collapse a csfmt_ensemble_v3 to a quantile-summary",
      "topics": [
        "ens_collapse",
        "ens_collapse.csfmt_ensemble_v3"
      ]
    },
    {
      "page": "mem_thresholds_v1",
      "title": "MEM intensity thresholds",
      "topics": [
        "mem_thresholds_v1",
        "mem_thresholds_v1.csfmt_ensemble_v3"
      ]
    },
    {
      "page": "nowcast_backtest",
      "title": "Replay a nowcast method across as-of weeks (backtest)",
      "topics": [
        "nowcast_backtest"
      ]
    },
    {
      "page": "nowcast_censor",
      "title": "Censor a reporting triangle to what was known \"as of\" a past week",
      "topics": [
        "nowcast_censor"
      ]
    },
    {
      "page": "nowcast_evaluate_v1",
      "title": "Evaluate nowcast method(s): interval coverage + point-estimate revision",
      "topics": [
        "nowcast_evaluate_v1"
      ]
    },
    {
      "page": "nowcast_passthrough_to_ensemble_v1",
      "title": "Build an ensemble from a reporting triangle WITHOUT nowcasting (passthrough)",
      "topics": [
        "nowcast_passthrough_to_ensemble_v1"
      ]
    },
    {
      "page": "nowcast_quasipoisson_v1",
      "title": "Nowcast a reporting triangle into an ensemble (quasipoisson reporting regression)",
      "topics": [
        "nowcast_quasipoisson_v1",
        "nowcast_quasipoisson_v1.csfmt_reporting_triangle_v3"
      ]
    },
    {
      "page": "nowcast_truth",
      "title": "The settled (eventually-observed) total per reference week",
      "topics": [
        "nowcast_truth"
      ]
    },
    {
      "page": "prediction_interval",
      "title": "Prediction thresholds",
      "topics": [
        "prediction_interval"
      ]
    },
    {
      "page": "prediction_interval.glm",
      "title": "Prediction thresholds",
      "topics": [
        "prediction_interval.glm"
      ]
    },
    {
      "page": "print.csfmt_ensemble_v3",
      "title": "Print a `csfmt_ensemble_v3`",
      "topics": [
        "print.csfmt_ensemble_v3"
      ]
    },
    {
      "page": "q_label",
      "title": "Probability -> controlled-vocabulary quantile label",
      "topics": [
        "q_label"
      ]
    },
    {
      "page": "q_value",
      "title": "Quantile label -> probability (inverse of [q_label])",
      "topics": [
        "q_value"
      ]
    },
    {
      "page": "qc_surveillance_data",
      "title": "Quality-control checks on surveillance input data",
      "topics": [
        "qc_surveillance_data"
      ]
    },
    {
      "page": "qc_week_over_week",
      "title": "Week-over-week QC: settled-data integrity (A) + frontier status signal (B)",
      "topics": [
        "qc_week_over_week"
      ]
    },
    {
      "page": "reporting_completion",
      "title": "Empirical reporting-completion summary from a reporting triangle",
      "topics": [
        "reporting_completion"
      ]
    },
    {
      "page": "reporting_completion_trend_v1",
      "title": "Reporting-completion trend: the delay curve by year and recent months",
      "topics": [
        "reporting_completion_trend_v1"
      ]
    },
    {
      "page": "reporting_triangle_matrix",
      "title": "Densify a reporting triangle into per-series reference x delay count matrices",
      "topics": [
        "reporting_triangle_matrix"
      ]
    },
    {
      "page": "rolling_slope_matrix",
      "title": "Rolling OLS slope over a weeks x draws matrix",
      "topics": [
        "rolling_slope_matrix"
      ]
    },
    {
      "page": "set_time_series_id",
      "title": "Assign content-hash time_series_id (+ readable label) by reference",
      "topics": [
        "set_time_series_id"
      ]
    },
    {
      "page": "short_term_trend",
      "title": "Determine the short term trend of a timeseries",
      "topics": [
        "short_term_trend",
        "short_term_trend.csfmt_ensemble_v3",
        "short_term_trend.csfmt_rts_data_v1"
      ]
    },
    {
      "page": "short_term_trend_sts_v1",
      "title": "Determine the short term trend of a surveillance time series",
      "topics": [
        "short_term_trend_sts_v1"
      ]
    },
    {
      "page": "signal_detection_hlm",
      "title": "Detect signals using the historical limits method",
      "topics": [
        "signal_detection_hlm",
        "signal_detection_hlm.csfmt_ensemble_v3",
        "signal_detection_hlm.csfmt_rts_data_v1"
      ]
    },
    {
      "page": "simulate_baseline_data",
      "title": "Simulate baseline surveillance data",
      "topics": [
        "simulate_baseline_data"
      ]
    },
    {
      "page": "simulate_seasonal_outbreak_data",
      "title": "Add seasonal outbreaks to simulated data",
      "topics": [
        "simulate_seasonal_outbreak_data"
      ]
    },
    {
      "page": "simulate_spike_outbreak_data",
      "title": "Add spiked outbreaks to simulated data",
      "topics": [
        "simulate_spike_outbreak_data"
      ]
    },
    {
      "page": "validate_ensemble",
      "title": "Validate a csfmt_ensemble_v3's invariants",
      "topics": [
        "validate_ensemble"
      ]
    }
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