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cff-version: 1.2.0
message: 'To cite package "bruno" in publications use:'
type: software
license: GPL-3.0-only
title: 'bruno: Predicting User-Defined Event Recurrence under Exchangeability'
version: 0.1.0
doi: 10.32614/CRAN.package.bruno
abstract: Implements analytical prediction of recurrence for user-defined binary events;
  'bruno' abbreviates Beta-Bernoulli Recurrence for Unobserved Next Outcomes. The
  procedure applies when the observed and future event indicators are judged exchangeable
  for the intended prediction. For an indefinitely extendible exchangeable binary
  sequence, de Finetti's representation theorem expresses the assigned joint probabilities
  as a mixture of Bernoulli laws over a mixing distribution on the unit interval (de
  Finetti, 1931) <https://doi.org/10.4064/fm-17-1-298-329>. The package adopts a beta
  distribution as an additional parametric specification of this mixing distribution.
  Users specify an initial probability mu0 assigned to the event and a positive concentration
  parameter tau, giving beta parameters a = mu0 * tau and b = (1 - mu0) * tau. If
  the declared event occurs s times among n observed cases, conditioning gives Beta(a
  + s, b + n - s). From this conditional assessment, the package computes analytically
  the probability assigned to occurrence of the same event in the next exchangeable
  case and, for a prespecified future sample size, the exact beta-binomial predictive
  distribution of the number of future event occurrences. Events may be supplied directly
  as logical or binary indicators or defined from paired pre-post measurements through
  a user-specified logical expression. Prediction may be performed for a single predictive
  class or separately across user-defined predictive classes, using common or class-specific
  initial probabilities and concentration parameters. Cases for which event status
  cannot be determined, and cases with missing predictive-class membership in grouped
  analyses, are excluded without imputation; case-level classification and inclusion
  information are retained for audit purposes. Summary methods provide central probability
  intervals for the conditional beta assessment and, for future samples larger than
  one case, predictive intervals for the future recurrence count. The package is intended
  for psychological, educational, pilot-study, and research decision-making applications
  in which recurrence of an explicitly defined event is the predictive target and
  the predictive relevance of observed cases for future cases can be substantively
  justified. The resulting probabilities concern recurrence of the declared event
  within the stated predictive class and do not independently establish latent change,
  intervention efficacy, causal effects, measurement validity, or a research decision.
authors:
- family-names: Gianeselli
  given-names: Irene
  email: irene.gianeselli@unibz.it
  orcid: https://orcid.org/0000-0002-8689-3035
repository: https://cran.r-universe.dev
commit: 9ca68d13cecaae32cbdf084ae2c8f038c512bbf7
date-released: '2026-08-21'
contact:
- family-names: Gianeselli
  given-names: Irene
  email: irene.gianeselli@unibz.it
  orcid: https://orcid.org/0000-0002-8689-3035