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  "Title": "Inverse Stable Prior for Widely-Used Exponential Models",
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  "Authors@R": "c(person(\"Dexter\", \"Cahoy\", role = c(\"aut\", \"cre\"), email = \"dexter.cahoy@gmail.com\"), person(\"Joseph\", \"Sedransk\", role = \"aut\", email = \"jxs123@case.edu\")  )",
  "Description": "Contains functions that allow Bayesian inference on a\nparameter of some widely-used exponential models. The functions\ncan generate independent samples from the closed-form posterior\ndistribution using the inverse stable prior. Inverse stable is\na non-conjugate prior for a parameter of an exponential\nsubclass of discrete and continuous data distributions (e.g.\nPoisson, exponential, inverse gamma, double exponential\n(Laplace), half-normal/half-Gaussian, etc.). The prior class\nprovides flexibility in capturing a wide array of prior beliefs\n(right-skewed and left-skewed) as modulated by a parameter that\nis bounded in (0,1). The generated samples can be used to\nsimulate the prior and posterior predictive distributions. More\ndetails can be found in Cahoy and Sedransk (2019)\n<doi:10.1007/s42519-018-0027-2>. The package can also be used\nas a teaching demo for introductory Bayesian courses.",
  "License": "GPL (>= 3)",
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  "Repository": "https://dcahoy.r-universe.dev",
  "Date/Publication": "2022-08-25 02:38:29 UTC",
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