Package: bruno
Title: Predicting User-Defined Event Recurrence under Exchangeability
Version: 0.1.0
Authors@R: 
    person(
        given = "Irene",
        family = "Gianeselli",
        role = c("aut", "cre"),
        email = "irene.gianeselli@unibz.it",
        comment = c(
            ORCID = "0000-0002-8689-3035"
        )
    )
Description: 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.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: stats
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
LazyData: true
NeedsCompilation: no
Config/roxygen2/version: 8.1.0
Packaged: 2026-08-19 21:13:49 UTC; irene
Author: Irene Gianeselli [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-8689-3035>)
Maintainer: Irene Gianeselli <irene.gianeselli@unibz.it>
Repository: CRAN
Date/Publication: 2026-08-21 13:30:38 UTC
