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  "Title": "Bayesian Dynamic Models for Poisson and Binomial Time Series",
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  "Description": "Fits Bayesian state-space models for non-Gaussian time\nseries using a latent log-rate (Poisson) or latent logit\n(binomial) formulation. The latent trajectory follows a\nfirst-order random walk or a stationary AR(1) process, sampled\nby Metropolis-within-Gibbs using the implied Gaussian Markov\nrandom field (GMRF) full conditionals. Four innovation\nstructures are supported for the latent increments:\nconstant-variance Gaussian, Student-t, a finite scale mixture\nof normals, and stochastic volatility. Both families support\ntime-constant zero inflation. The package provides simulation,\nfitting, forecasting, summary and plotting tools. It implements\nand extends the methodology of Zens and Bijak (2026)\n<doi:10.1214/26-AOAS2171>.",
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  "Author": "Gregor Zens [aut, cre]",
  "Maintainer": "Gregor Zens <zens@iiasa.ac.at>",
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    "forecast",
    "plot_fitted",
    "plot_forecast",
    "plot_latent",
    "plot_zero_inflation",
    "simulate_dynamic_binomial",
    "simulate_dynamic_poisson",
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      "title": "Weekly Mediterranean crossings (Mediterranean example)",
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      "page": "dynamic_prior",
      "title": "Specify priors for a dynamic count / binomial model",
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    {
      "page": "fit_dynamic_model",
      "title": "Fit a Bayesian dynamic count / binomial time-series model",
      "topics": [
        "fit_dynamic_model"
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      "page": "forecast",
      "title": "Generic forecast function",
      "topics": [
        "forecast"
      ]
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      "page": "forecast.dynamic_fit",
      "title": "Forecast a fitted dynamic model",
      "topics": [
        "forecast.dynamic_fit"
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    {
      "page": "med_weekly",
      "title": "Weekly Mediterranean crossings (Mediterranean example)",
      "topics": [
        "med_weekly"
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    {
      "page": "plot_fitted",
      "title": "Plot observed versus fitted values",
      "topics": [
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      "page": "plot_forecast",
      "title": "Plot observed history, fitted values and forecast with uncertainty",
      "topics": [
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    {
      "page": "plot_latent",
      "title": "Plot the fitted latent trajectory",
      "topics": [
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      "page": "plot_zero_inflation",
      "title": "Plot zero-inflation diagnostics",
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      "title": "Plot method for fitted dynamic models",
      "topics": [
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      "title": "In-sample fitted values and posterior predictive replicates",
      "topics": [
        "predict.dynamic_fit"
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    },
    {
      "page": "simulate_dynamic_binomial",
      "title": "Simulate a binomial dynamic series",
      "topics": [
        "simulate_dynamic_binomial"
      ]
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    {
      "page": "simulate_dynamic_poisson",
      "title": "Simulate a Poisson dynamic series",
      "topics": [
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    {
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      "title": "Posterior probability that each observed zero is structural",
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      "title": "Summarise a fitted dynamic model",
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      "title": "Dynamic Models for Poisson and Binomial Time Series",
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      "headings": [
        "Introduction",
        "Simulating data",
        "Fitting a Poisson model (no zero inflation)",
        "Forecasting",
        "AR(1) latent dynamics",
        "Offset",
        "A robust (Student-t) fit on the example data",
        "Zero inflation and structural zeros",
        "Conditional versus unconditional fits and replicates",
        "A binomial model with known trials",
        "Choosing and changing priors",
        "References"
      ],
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      "modified": "2026-07-14 18:33:59",
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