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  "Title": "No-U-Turn MCMC Sampling for 'ADMB' Models",
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  "Description": "Bayesian inference using the no-U-turn (NUTS) algorithm by\nHoffman and Gelman (2014)\n<https://www.jmlr.org/papers/v15/hoffman14a.html>. Designed for\n'AD Model Builder' ('ADMB') models, or when R functions for\nlog-density and log-density gradient are available, such as\n'Template Model Builder' models and other special cases.\nFunctionality is similar to 'Stan', and the 'rstan' and\n'shinystan' packages are used for diagnostics and inference.",
  "URL": "https://github.com/Cole-Monnahan-NOAA/adnuts,\nhttps://cole-monnahan-noaa.github.io/adnuts/",
  "BugReports": "https://github.com/Cole-Monnahan-NOAA/adnuts/issues",
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  "Repository": "https://cole-monnahan-noaa.r-universe.dev",
  "Date/Publication": "2026-04-03 16:32:13 UTC",
  "RemoteUrl": "https://github.com/cole-monnahan-noaa/adnuts",
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  "Maintainer": "Cole Monnahan <monnahc@uw.edu>",
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    "check_identifiable",
    "extract_sampler_params",
    "extract_samples",
    "is.adfit",
    "launch_shinyadmb",
    "pairs_admb",
    "plot_marginals",
    "plot_sampler_params",
    "plot_uncertainties",
    "sample_admb",
    "sample_inits",
    "sample_nuts",
    "sample_rwm",
    "sample_tmb"
  ],
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    {
      "page": "dot-check_ADMB_version",
      "title": "Check that the model is compiled with the right version of ADMB which is 12.0 or later",
      "topics": [
        ".check_ADMB_version"
      ]
    },
    {
      "page": "dot-check_console_printing",
      "title": "Check if the session is interactive or Rstudio which has implications for parallel output",
      "topics": [
        ".check_console_printing"
      ]
    },
    {
      "page": "dot-check_model_path",
      "title": "Check that the file can be found",
      "topics": [
        ".check_model_path"
      ]
    },
    {
      "page": "dot-getADMBHessian",
      "title": "Read in admodel.hes file",
      "topics": [
        ".getADMBHessian"
      ]
    },
    {
      "page": "dot-sample_admb",
      "title": "Hidden wrapper function for sampling from ADMB models",
      "topics": [
        ".sample_admb"
      ]
    },
    {
      "page": "dot-update_model",
      "title": "Convert model name depending on system",
      "topics": [
        ".update_model"
      ]
    },
    {
      "page": "adfit",
      "title": "Constructor for the \"adfit\" (A-D fit) class",
      "topics": [
        "adfit"
      ]
    },
    {
      "page": "adnuts",
      "title": "adnuts: No-U-turn sampling for AD Model Builder (ADMB)",
      "topics": [
        "adnuts"
      ]
    },
    {
      "page": "as.data.frame.adfit",
      "title": "Convert object of class adfit to data.frame. Calls 'extract_samples'",
      "topics": [
        "as.data.frame.adfit"
      ]
    },
    {
      "page": "check_identifiable",
      "title": "Check identifiability from model Hessian",
      "topics": [
        "check_identifiable"
      ]
    },
    {
      "page": "extract_sampler_params",
      "title": "Extract sampler parameters from a fit.",
      "topics": [
        "extract_sampler_params"
      ]
    },
    {
      "page": "extract_samples",
      "title": "Extract posterior samples from a model fit.",
      "topics": [
        "extract_samples"
      ]
    },
    {
      "page": "is.adfit",
      "title": "Check object of class adfit",
      "topics": [
        "is.adfit"
      ]
    },
    {
      "page": "launch_shinyadmb",
      "title": "Launch shinystan for an ADMB fit.",
      "topics": [
        "launch_shinyadmb"
      ]
    },
    {
      "page": "launch_shinytmb",
      "title": "Launch shinystan for a TMB fit.",
      "topics": [
        "launch_shinytmb"
      ]
    },
    {
      "page": "pairs_admb",
      "title": "Plot pairwise parameter posteriors and optionally the MLE points and confidence ellipses.",
      "topics": [
        "pairs_admb"
      ]
    },
    {
      "page": "plot_marginals",
      "title": "Plot marginal distributions for a fitted model",
      "topics": [
        "plot_marginals"
      ]
    },
    {
      "page": "plot_sampler_params",
      "title": "Plot adaptation metrics for a fitted model.",
      "topics": [
        "plot_sampler_params"
      ]
    },
    {
      "page": "plot_uncertainties",
      "title": "Plot MLE vs MCMC marginal standard deviations for each parameter",
      "topics": [
        "plot_uncertainties"
      ]
    },
    {
      "page": "plot.adfit",
      "title": "Plot object of class adfit",
      "topics": [
        "plot.adfit"
      ]
    },
    {
      "page": "print.adfit",
      "title": "Print summary of adfit object",
      "topics": [
        "print.adfit"
      ]
    },
    {
      "page": "sample_admb",
      "title": "Deprecated version of wrapper function. Use sample_nuts or sample_rwm instead.",
      "topics": [
        "sample_admb"
      ]
    },
    {
      "page": "sample_inits",
      "title": "Function to generate random initial values from a previous fit using adnuts",
      "topics": [
        "sample_inits"
      ]
    },
    {
      "page": "wrappers",
      "title": "Bayesian inference of an ADMB model using the no-U-turn sampler (NUTS) or random walk Metropolis (RWM) algorithms.",
      "topics": [
        "sample_nuts",
        "sample_rwm",
        "wrappers"
      ]
    },
    {
      "page": "sample_tmb",
      "title": "Bayesian inference of a TMB model using the no-U-turn sampler.",
      "topics": [
        "sample_tmb"
      ]
    },
    {
      "page": "sample_tmb_hmc",
      "title": "Draw MCMC samples from a model posterior using a static HMC sampler.",
      "topics": [
        "sample_tmb_hmc"
      ]
    },
    {
      "page": "sample_tmb_nuts",
      "title": "Draw MCMC samples from a model posterior using the No-U-Turn (NUTS) sampler with dual averaging.",
      "topics": [
        "sample_tmb_nuts"
      ]
    },
    {
      "page": "sample_tmb_rwm",
      "title": "[Deprecated] Draw MCMC samples from a model posterior using a Random Walk Metropolis (RWM) sampler.",
      "topics": [
        "sample_tmb_rwm"
      ]
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      "title": "Print summary of object of class adfit",
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      ]
    }
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      "title": "No-U-turn sampling for ADMB models",
      "author": "Cole C. Monnahan",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Summary",
        "Sampling for ADMB models",
        "Setting up the model",
        "Sampling with sample_nuts and sample_rwm",
        "mceval phase and posterior outputs",
        "Bounds & Priors",
        "Initializing chains",
        "Parallel sampling",
        "Diagnostics and plotting results",
        "Diagnosing MCMC chains",
        "Plotting output",
        "Mass matrix adaptation",
        "Sampling for TMB models",
        "The no-U-turn sampler implementation",
        "Brief review of Hamiltonian Monte Carlo",
        "Algorithm implementation details",
        "User intervention",
        "Algorithm validity",
        "References"
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      "modified": "2026-04-03 15:45:54",
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