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  "Title": "Bayesian BIN (Bias, Information, Noise) Model of Forecasting",
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  "Authors@R": "c(person(given = \"Ville\",\nfamily = \"Satopää\",\nrole = c(\"aut\", \"cre\"),\nemail = \"ville.satopaa@gmail.com\",\ncomment = c(ORCID = \"https://orcid.org/0000-0002-3787-7303\")),\nperson(given = \"Marat\",\nfamily = \"Salikhov\",\nrole = \"aut\"),\nperson(given = \"Elvira\",\nfamily = \"Moreno\",\nrole = \"aut\"))",
  "Description": "A recently proposed Bayesian BIN model disentangles the\nunderlying processes that enable forecasters and forecasting\nmethods to improve, decomposing forecasting accuracy into three\ncomponents: bias, partial information, and noise. By describing\nthe differences between two groups of forecasters, the model\nallows the user to carry out useful inference, such as\ncalculating the posterior probabilities of the treatment\nreducing bias, diminishing noise, or increasing information. It\nalso provides insight into how much tamping down bias and noise\nin judgment or enhancing the efficient extraction of valid\ninformation from the environment improves forecasting accuracy.\nThis package provides easy access to the BIN model. For further\ninformation refer to the paper Ville A. Satopää, Marat\nSalikhov, Philip E. Tetlock, and Barbara Mellers (2021) \"Bias,\nInformation, Noise: The BIN Model of Forecasting\"\n<doi:10.1287/mnsc.2020.3882>.",
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      "fileid": "https://r2.ropensci.org/d763bd36a95cc9c8ce0507097f4d4ed8b357d0f8dd01a2de4ff0aa78a0a2d334",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/satopaa/actions/runs/37782567712"
    }
  ]
}