{
  "_id": "6ad1efc129625c14e47af1df",
  "Package": "diseasenowcasting",
  "Title": "Nowcast Time Series of Epidemiological Cases",
  "Version": "2.4.0",
  "Authors@R": "c(person(\"Rodrigo\", \"Zepeda-Tello\", role = c(\"aut\", \"cre\"),\nemail = \"rzepeda17@gmail.com\",\ncomment = c(ORCID = \"0000-0003-4471-5270\")),\nperson(\"Rami\", \"Yaari\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-8808-8937\")),\nperson(\"Matteo\", \"Perini\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-9465-6216\")),\nperson(\"Tal\", \"Robin\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-3128-2499\")),\nperson(\"Teresa\", \"Yamana\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-8349-3151\")),\nperson(\"Jeffrey\", \"Shaman\", role = \"ctb\"),\nperson(given = \"Columbia University in the City of New York\",\nrole = c(\"cph\", \"fnd\")))",
  "Description": "Implements Bayesian nowcasting models for incident disease\ncases using a censored point-process approach. The reporting\ndelay is modelled directly as a stochastic process, jointly\nwith the epidemic dynamics, via a censored likelihood.\nInference uses RTMB (CppAD autodiff + Laplace approximation).\nSupports arbitrary epidemic models (including HSGP, AR1, SIR),\narbitrary delay families (including LogNormal, GenGamma,\nDirichlet), stratified data, extreme-delay detection, and\nbacktesting.",
  "License": "MIT + file LICENSE",
  "URL": "https://rodrigozepeda.github.io/diseasenowcasting/,\nhttps://github.com/RodrigoZepeda/diseasenowcasting",
  "BugReports": "https://github.com/RodrigoZepeda/diseasenowcasting/issues",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
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  "Repository": "https://rodrigozepeda.r-universe.dev",
  "Date/Publication": "2026-09-09 18:55:43 UTC",
  "RemoteUrl": "https://github.com/rodrigozepeda/diseasenowcasting",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-16 08:53:25 UTC",
    "User": "root"
  },
  "Author": "Rodrigo Zepeda-Tello [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4471-5270>),\nRami Yaari [aut] (ORCID: <https://orcid.org/0000-0002-8808-8937>),\nMatteo Perini [aut] (ORCID: <https://orcid.org/0000-0002-9465-6216>),\nTal Robin [ctb] (ORCID: <https://orcid.org/0000-0002-3128-2499>),\nTeresa Yamana [ctb] (ORCID: <https://orcid.org/0000-0001-8349-3151>),\nJeffrey Shaman [ctb],\nColumbia University in the City of New York [cph, fnd]",
  "Maintainer": "Rodrigo Zepeda-Tello <rzepeda17@gmail.com>",
  "_user": "rodrigozepeda",
  "_type": "src",
  "_file": "diseasenowcasting_2.4.0.tar.gz",
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  "_expires": "2027-01-24T09:34:55.000Z",
  "_created": "2026-10-16T08:53:25.000Z",
  "_published": "2026-10-16T09:34:57.617Z",
  "_buildurl": "https://github.com/r-universe/rodrigozepeda/actions/runs/38876104759",
  "_status": "success",
  "_host": "GitHub-Actions",
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    "message": "Merge pull request #139 from RodrigoZepeda/skellam\n\nUpdated development skill",
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  "_exports": [
    "ar1_epidemic",
    "auto_nowcast",
    "autoplot",
    "backtest",
    "best_model",
    "best_model_name",
    "best_score",
    "beta_prior",
    "cauchy_prior",
    "chi_square_prior",
    "comparison_scores",
    "cumulative_process",
    "custom_delay",
    "custom_epidemic",
    "default_priors",
    "dirichlet_delay",
    "dirichlet_revision",
    "dn_palette",
    "double_exponential_prior",
    "exponential_prior",
    "extreme_values",
    "fit",
    "fit_check",
    "fix_param",
    "flat_prior",
    "gamma_delay",
    "gamma_prior",
    "gamma_revision",
    "generalized_gamma_delay",
    "generalized_gamma_revision",
    "half_cauchy_prior",
    "half_double_exponential_prior",
    "half_normal_prior",
    "half_std_normal_prior",
    "half_student_t_prior",
    "hsgp_epidemic",
    "infer_max_time",
    "inv_gamma_prior",
    "load_nowcast",
    "logistic_prior",
    "lognormal_delay",
    "lognormal_prior",
    "lognormal_revision",
    "model",
    "nb_likelihood",
    "normal_prior",
    "nowcast",
    "nowcast_diagnostic",
    "nowcast_twostage",
    "parameters",
    "poisson_likelihood",
    "positive_flat_prior",
    "prepare_data",
    "revision_process",
    "sample",
    "save_nowcast",
    "selection_metric",
    "selection_timings",
    "sir_epidemic",
    "std_normal_prior",
    "student_t_prior",
    "summarise_nowcast_matrix",
    "surprise",
    "theme_diseasenowcasting",
    "validate_custom_delay",
    "validate_custom_epidemic",
    "weibull_prior"
  ],
  "_datasets": [
    {
      "name": "covid_colombia",
      "title": "COVID-19 Notifications - Colombia 2020-2023",
      "object": "covid_colombia",
      "class": [
        "data.frame"
      ],
      "fields": [
        "notification_date",
        "diagnosis_date",
        "sex",
        "n"
      ],
      "rows": 35501,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "auto_nowcast",
      "title": "Automatically select and fit the best nowcasting model",
      "topics": [
        "auto_nowcast"
      ]
    },
    {
      "page": "backtest",
      "title": "Backtest one or more diseasenowcasting models",
      "topics": [
        "backtest"
      ]
    },
    {
      "page": "best_model",
      "title": "The winning 'model()' object from a nowcast",
      "topics": [
        "best_model"
      ]
    },
    {
      "page": "best_model_name",
      "title": "Name of the model chosen by 'auto_nowcast()'",
      "topics": [
        "best_model_name"
      ]
    },
    {
      "page": "best_score",
      "title": "The scoreboard row for the model 'auto_nowcast()' chose",
      "topics": [
        "best_score"
      ]
    },
    {
      "page": "comparison_scores",
      "title": "The model-selection scoreboard from 'auto_nowcast()'",
      "topics": [
        "comparison_scores"
      ]
    },
    {
      "page": "covid_colombia",
      "title": "COVID-19 Notifications - Colombia 2020-2023",
      "topics": [
        "covid_colombia"
      ]
    },
    {
      "page": "cumulative_process",
      "title": "Count-cumulative observation process",
      "topics": [
        "cumulative_process"
      ]
    },
    {
      "page": "custom_delay",
      "title": "User-defined delay distribution",
      "topics": [
        "custom_delay"
      ]
    },
    {
      "page": "custom_epidemic",
      "title": "User-defined epidemic process",
      "topics": [
        "custom_epidemic"
      ]
    },
    {
      "page": "default_priors",
      "title": "Build the default prior bundle for an RTMB nowcast model",
      "topics": [
        "default_priors"
      ]
    },
    {
      "page": "delay_process",
      "title": "Delay distribution for the Bayesian Nowcast",
      "topics": [
        "delay_process",
        "dirichlet_delay",
        "gamma_delay",
        "generalized_gamma_delay",
        "lognormal_delay"
      ]
    },
    {
      "page": "diseasenowcasting_workflows",
      "title": "Native and cross-engine workflows",
      "topics": [
        "diseasenowcasting_workflows"
      ]
    },
    {
      "page": "dn_palette",
      "title": "diseasenowcasting colour palette",
      "topics": [
        "dn_palette"
      ]
    },
    {
      "page": "epidemic_process",
      "title": "Epidemic process for the Bayesian Nowcast",
      "topics": [
        "ar1_epidemic",
        "epidemic_process",
        "hsgp_epidemic",
        "sir_epidemic"
      ]
    },
    {
      "page": "extreme_values",
      "title": "Surprising (extreme) values flagged during the last 'update()'",
      "topics": [
        "extreme_values"
      ]
    },
    {
      "page": "fit",
      "title": "Fit a nowcast model with the RTMB engine",
      "topics": [
        "fit"
      ]
    },
    {
      "page": "fit_check",
      "title": "Check RTMB optimizer diagnostics for a fitted nowcast",
      "topics": [
        "fit_check"
      ]
    },
    {
      "page": "fix_param",
      "title": "Hard-fix a parameter in a prior bundle (treat as data, drop from estimation)",
      "topics": [
        "fix_param"
      ]
    },
    {
      "page": "infer_max_time",
      "title": "Infer 'max_time' for custom epidemic processes",
      "topics": [
        "infer_max_time"
      ]
    },
    {
      "page": "likelihood",
      "title": "Likelihood for the Bayesian Nowcast",
      "topics": [
        "likelihood",
        "nb_likelihood",
        "poisson_likelihood"
      ]
    },
    {
      "page": "load_nowcast",
      "title": "Load a nowcast saved with 'save_nowcast()'",
      "topics": [
        "load_nowcast"
      ]
    },
    {
      "page": "model",
      "title": "Bayesian Nowcast Model",
      "topics": [
        "model"
      ]
    },
    {
      "page": "nowcast",
      "title": "Fit a nowcast model to censored reporting data",
      "topics": [
        "nowcast"
      ]
    },
    {
      "page": "nowcast_diagnostic",
      "title": "Three-panel diagnostic plot for a fitted nowcast",
      "topics": [
        "nowcast_diagnostic"
      ]
    },
    {
      "page": "nowcast_twostage",
      "title": "Two-stage multiple-imputation nowcast",
      "topics": [
        "nowcast_twostage"
      ]
    },
    {
      "page": "parameters",
      "title": "Parameter estimates from a fitted nowcast",
      "topics": [
        "parameters"
      ]
    },
    {
      "page": "prepare_data",
      "title": "Prepare data for the RTMB nowcast engine",
      "topics": [
        "prepare_data"
      ]
    },
    {
      "page": "priors",
      "title": "Priors for model parameters",
      "topics": [
        "beta_prior",
        "cauchy_prior",
        "chi_square_prior",
        "double_exponential_prior",
        "exponential_prior",
        "flat_prior",
        "gamma_prior",
        "half_cauchy_prior",
        "half_double_exponential_prior",
        "half_normal_prior",
        "half_std_normal_prior",
        "half_student_t_prior",
        "inv_gamma_prior",
        "logistic_prior",
        "lognormal_prior",
        "normal_prior",
        "positive_flat_prior",
        "priors",
        "std_normal_prior",
        "student_t_prior",
        "weibull_prior"
      ]
    },
    {
      "page": "revision_delay",
      "title": "Revision-lag distributions",
      "topics": [
        "dirichlet_revision",
        "gamma_revision",
        "generalized_gamma_revision",
        "lognormal_revision",
        "revision_delay"
      ]
    },
    {
      "page": "revision_process",
      "title": "Revision process: reports that are later confirmed or retracted",
      "topics": [
        "revision_process"
      ]
    },
    {
      "page": "sample",
      "title": "Draw random samples from a prior (or fall back to 'base::sample()')",
      "topics": [
        "sample"
      ]
    },
    {
      "page": "save_nowcast",
      "title": "Save a fitted nowcast to disk",
      "topics": [
        "save_nowcast"
      ]
    },
    {
      "page": "selection_metric",
      "title": "The metric 'auto_nowcast()' used to pick the winner",
      "topics": [
        "selection_metric"
      ]
    },
    {
      "page": "selection_timings",
      "title": "Fitting times recorded by 'auto_nowcast()'",
      "topics": [
        "selection_timings"
      ]
    },
    {
      "page": "summarise_nowcast_matrix",
      "title": "Quantile-table summary of a pooled nowcast draws matrix",
      "topics": [
        "summarise_nowcast_matrix"
      ]
    },
    {
      "page": "surprise",
      "title": "Compute surprise scores for new observations",
      "topics": [
        "surprise"
      ]
    },
    {
      "page": "surprise.list",
      "title": "Surprise score on a raw fit() result",
      "topics": [
        "surprise.list"
      ]
    },
    {
      "page": "theme_diseasenowcasting",
      "title": "ggplot2 theme matching the diseasenowcasting visual identity",
      "topics": [
        "theme_diseasenowcasting"
      ]
    },
    {
      "page": "validate_custom_delay",
      "title": "Validate a custom delay distribution for RTMB traceability",
      "topics": [
        "validate_custom_delay"
      ]
    },
    {
      "page": "validate_custom_epidemic",
      "title": "Validate a user-defined epidemic process for RTMB traceability",
      "topics": [
        "validate_custom_epidemic"
      ]
    }
  ],
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      "author": "diseasenowcasting team",
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      "headings": [
        "The one rule: RTMB",
        "1. Custom delay: Weibull",
        "The data",
        "The custom part: a Weibull delay",
        "Fit it (and a built-in to compare against)",
        "2.  Custom epidemic: different SIR",
        "The custom part: an SIR epidemic",
        "Fit it",
        "3. Summary: Tips for writing your own component",
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        "Example 3 -- Comparing models with a backtest",
        "When a report is not yet a case: the revision process",
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      "author": "diseasenowcasting team",
      "engine": "knitr::rmarkdown",
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        "Quick reference",
        "What the rest of the vignette shows:",
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        "2.1 Log-Normal delay",
        "2.1.1 Location (mu)",
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      "created": "2026-06-04 20:35:21",
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