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  "Package": "incidental",
  "Title": "Implements Empirical Bayes Incidence Curves",
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  "Description": "Make empirical Bayes incidence curves from reported case\ndata using a specified delay distribution.",
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  "Maintainer": "Lauren Hannah <lauren_hannah@apple.com>",
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    {
      "page": "compute_expected_cases",
      "title": "Compute expected cases",
      "topics": [
        "compute_expected_cases"
      ]
    },
    {
      "page": "compute_log_incidence",
      "title": "Compute log likelihood of incidence model",
      "topics": [
        "compute_log_incidence"
      ]
    },
    {
      "page": "covid_delay_dist",
      "title": "Delay distribution from COVID-19 pandemic.",
      "topics": [
        "covid_delay_dist"
      ]
    },
    {
      "page": "covid_new_york_city",
      "title": "New York City data from the COVID-19 pandemic.",
      "topics": [
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    },
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      "page": "data_check",
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    {
      "page": "data_processing",
      "title": "Data processing wrapper",
      "topics": [
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      ]
    },
    {
      "page": "diff_trans",
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      ]
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    {
      "page": "fit_incidence",
      "title": "Fit incidence curve to reported data",
      "topics": [
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    {
      "page": "front_zero_pad",
      "title": "Pad reported data with zeros in front",
      "topics": [
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    {
      "page": "incidence_to_df",
      "title": "Export incidence model to data frame",
      "topics": [
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    },
    {
      "page": "init_params",
      "title": "Initialize spline parameters (beta)",
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      "title": "Make AR samples for extrapolation past end point",
      "topics": [
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      ]
    },
    {
      "page": "make_likelihood_matrix",
      "title": "Make delay likelihood matrix",
      "topics": [
        "make_likelihood_matrix"
      ]
    },
    {
      "page": "make_spline_basis",
      "title": "Create spline basis matrix",
      "topics": [
        "make_spline_basis"
      ]
    },
    {
      "page": "marg_loglike_poisson",
      "title": "Marginal log likelihood This function computes the marginal probability of Pr(reported | beta).  Note that lnPmat must be zero padded enough (or censored) to match the length of reported cases vector.",
      "topics": [
        "marg_loglike_poisson"
      ]
    },
    {
      "page": "marg_loglike_poisson_fisher",
      "title": "Marginal log likelihood Fisher information matrix",
      "topics": [
        "marg_loglike_poisson_fisher"
      ]
    },
    {
      "page": "marg_loglike_poisson_grad",
      "title": "Marginal log likelihood gradient",
      "topics": [
        "marg_loglike_poisson_grad"
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    },
    {
      "page": "plot.incidence_spline_model",
      "title": "Plot model from fit_incidence",
      "topics": [
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      ]
    },
    {
      "page": "poisson_objective",
      "title": "Poisson objective function",
      "topics": [
        "poisson_objective"
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    },
    {
      "page": "poisson_objective_grad",
      "title": "Poisson objective function gradient",
      "topics": [
        "poisson_objective_grad"
      ]
    },
    {
      "page": "poisson_objective_post_cov_approx",
      "title": "Compute Fisher information matrix for Poisson objective",
      "topics": [
        "poisson_objective_post_cov_approx"
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    },
    {
      "page": "regfun",
      "title": "Beta regularization function",
      "topics": [
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      "page": "regfun_grad",
      "title": "Beta regularization function gradient",
      "topics": [
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