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  "Author": "Alexander Keil [aut, cre]",
  "Maintainer": "Alexander Keil <akeil@unc.edu>",
  "Description": "Learning and using the Metropolis algorithm for Bayesian\nfitting of a generalized linear model. The package vignette\nincludes examples of hand-coding a logistic model using several\nvariants of the Metropolis algorithm. The package also contains\nR functions for simulating posterior distributions of Bayesian\ngeneralized linear model parameters using guided, adaptive,\nguided-adaptive and random walk Metropolis algorithms. The\nrandom walk Metropolis algorithm was originally described in\nMetropolis et al (1953); <doi:10.1063/1.1699114>.",
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    {
      "page": "as.mcmc.metropolis.samples",
      "title": "Convert glm_metropolis output to 'mcmc' object from package coda",
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      "title": "Inverse logit transform",
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      "title": "logistic log likelihood",
      "topics": [
        "logistic_ll"
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    },
    {
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      "title": "A case control study of childhood leukemia and magnetic fields from Savitz, Wachtel, Barnes, et al (1998) doi:10.1093/oxfordjournals.aje.a114943 <https://doi.org/10.1093/oxfordjournals.aje.a114943>.",
      "topics": [
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      "title": "Gaussian log likelihood",
      "topics": [
        "normal_ll"
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    {
      "page": "plot.metropolis.samples",
      "title": "Plot the output from the metropolis function",
      "topics": [
        "plot.metropolis.samples"
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    },
    {
      "page": "print.metropolis.samples",
      "title": "Print a metropolis.samples object",
      "topics": [
        "print.metropolis.samples"
      ]
    },
    {
      "page": "summary.metropolis.samples",
      "title": "Summarize a probability distribution from a Markov Chain",
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      "source": "metropolis-vignette.Rmd",
      "filename": "metropolis-vignette.html",
      "title": "metropolis",
      "author": "Alexander Keil",
      "engine": "knitr::knitr",
      "headings": [
        "A guided walk through the Metropolis algorithmm",
        "The data",
        "Helper functions",
        "Maximum likelihood estimates",
        "Random walk metropolis",
        "Inspecting output",
        "Guided metropolis",
        "Contrasting output with random walk",
        "Guided, adaptive metropolis algorithm",
        "Contrasting output",
        "Guided, adaptive metropolis algorithm using normal priors",
        "Using the R package",
        "Using the R package, smart initial values",
        "Extending the logistic model results after samples are generated"
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      "modified": "2020-01-23 16:30:07",
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