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.