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The Bayesian Mission Mean (BMM) is\nused to evaluate a question of interest (that is, a mean that\nrandomly selects combination of factor levels based on their\nprobability of occurring instead of averaging over the factor\nlevels, as in the grand mean). Under this construct, in\ncontrast to a Gibbs sampler (or Metropolis-within-Gibbs\nsampler), a two-stage sampling method is required. The nested\nsampler determines the conditional posterior distribution of\nthe model parameters, given Y, and the outside sampler\ndetermines the marginal posterior distribution of Y (also\ncommonly called the predictive distribution for Y). This\napproach provides a sample from the joint posterior\ndistribution of Y and the model parameters, while also\naccounting for the threshold value that must be obtained in\norder for the question of interest to be evaluated as\nsuccessful.", "URL": "https://github.com/jcliff89/ContRespPP", "BugReports": "https://github.com/jcliff89/ContRespPP/issues", "License": "CC0", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.1.1", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Repository": "https://jcliff89.r-universe.dev", "Date/Publication": "2022-10-13 23:37:44 UTC", "RemoteUrl": "https://github.com/jcliff89/contresppp", "RemoteRef": "HEAD", "RemoteSha": "de7f162c5412e6a889fcc52d16da32c19f74c0e9", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-07 06:38:00 UTC", "User": "root" }, "Author": "Victoria Sieck [aut, cre] (ORCID:\n),\nJoshua Clifford [aut] (ORCID: ),\nFletcher Christensen [aut] (ORCID:\n)", "Maintainer": 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