bruno: Predicting User-Defined Event Recurrence under Exchangeability
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)
<doi: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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
stats |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2026-08-21 |
| DOI: |
10.32614/CRAN.package.bruno (may not be active yet) |
| Author: |
Irene Gianeselli 
[aut, cre] |
| Maintainer: |
Irene Gianeselli <irene.gianeselli at unibz.it> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
bruno results |
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