{ "_id": "6aafaa383d719c1f84080765", "Package": "bruno", "Title": "Predicting User-Defined Event Recurrence under Exchangeability", "Version": "0.1.0", "Authors@R": "person(\ngiven = \"Irene\",\nfamily = \"Gianeselli\",\nrole = c(\"aut\", \"cre\"),\nemail = \"irene.gianeselli@unibz.it\",\ncomment = c(\nORCID = \"0000-0002-8689-3035\"\n)\n)", "Description": "Implements analytical prediction of recurrence for\nuser-defined binary events; 'bruno' abbreviates Beta-Bernoulli\nRecurrence for Unobserved Next Outcomes. The procedure applies\nwhen the observed and future event indicators are judged\nexchangeable for the intended prediction. For an indefinitely\nextendible exchangeable binary sequence, de Finetti's\nrepresentation theorem expresses the assigned joint\nprobabilities as a mixture of Bernoulli laws over a mixing\ndistribution on the unit interval (de Finetti, 1931)\n. The package adopts a beta\ndistribution as an additional parametric specification of this\nmixing distribution. Users specify an initial probability mu0\nassigned to the event and a positive concentration parameter\ntau, giving beta parameters a = mu0 * tau and b = (1 - mu0) *\ntau. If the declared event occurs s times among n observed\ncases, conditioning gives Beta(a + s, b + n - s). From this\nconditional assessment, the package computes analytically the\nprobability assigned to occurrence of the same event in the\nnext exchangeable case and, for a prespecified future sample\nsize, the exact beta-binomial predictive distribution of the\nnumber of future event occurrences. Events may be supplied\ndirectly as logical or binary indicators or defined from paired\npre-post measurements through a user-specified logical\nexpression. Prediction may be performed for a single predictive\nclass or separately across user-defined predictive classes,\nusing common or class-specific initial probabilities and\nconcentration parameters. Cases for which event status cannot\nbe determined, and cases with missing predictive-class\nmembership in grouped analyses, are excluded without\nimputation; case-level classification and inclusion information\nare retained for audit purposes. Summary methods provide\ncentral probability intervals for the conditional beta\nassessment and, for future samples larger than one case,\npredictive intervals for the future recurrence count. The\npackage is intended for psychological, educational,\npilot-study, and research decision-making applications in which\nrecurrence of an explicitly defined event is the predictive\ntarget and the predictive relevance of observed cases for\nfuture cases can be substantively justified. The resulting\nprobabilities concern recurrence of the declared event within\nthe stated predictive class and do not independently establish\nlatent change, intervention efficacy, causal effects,\nmeasurement validity, or a research decision.", "License": "GPL-3", "Encoding": "UTF-8", "Config/testthat/edition": "3", "LazyData": "true", "NeedsCompilation": "no", "Config/roxygen2/version": "8.1.0", "Packaged": { "Date": "2026-09-20 09:38:41 UTC", "User": "root" }, "Author": "Irene Gianeselli [aut, cre] (ORCID:\n)", "Maintainer": "Irene Gianeselli ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-08-21 13:30:38 UTC", "RemoteUrl": "https://github.com/cran/bruno", "RemoteRef": "HEAD", "RemoteSha": "9ca68d13cecaae32cbdf084ae2c8f038c512bbf7", "_user": "cran", "_type": "src", "_file": "bruno_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/3ac3ef61f13b60d23167413f8d4590cbedce387a2a70c81843f7d8aea3d542f0", "_filesize": 205429, "_sha256": 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