Bayesian methods for predicting the calendar time at which a target number of events is reached in clinical trials. The methodology applies to both blinded and unblinded settings and jointly models enrollment, event-time, and censoring processes. The package provides tools for trial data simulation, model fitting using 'Stan' via the 'rstan' interface, and event time prediction under a wide range of trial designs, including varying sample sizes, enrollment patterns, treatment effects, and event or censoring time distributions. The package is intended to support interim monitoring, operational planning, and decision-making in clinical trial development. Methods are described in Fu et al. (2025) <doi:10.1002/sim.70310>.

Fits a piecewise exponential hazard to survival data using a
    Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
    formulation for the spatial dependency in the hazard rates for each piece.
    This function uses Metropolis- Hastings-Green MCMC to allow the number of split
    points to vary and also uses Stochastic Search Variable Selection to determine
    what covariates drive the risk of the event. This function outputs trace plots
    depicting the number of split points in the hazard and the number of variables
    included in the hazard. The function saves all posterior quantities to the
    desired path.

Models time-to-event data from interval-censored
    screening studies. It accounts for latent prevalence at baseline and
    incorporates misclassification due to imperfect test sensitivity. For usage
    details, see the package vignette "BayesPIM_intro". Further details can be
    found in Klausch, Lissenberg-Witte and Coupé (2026)
    <doi:10.1002/sim.70433>.

A lightweight modelling syntax for defining likelihoods and priors
    and for computing Bayes factors for simple one parameter models. It includes
    functionality for computing and plotting priors, likelihoods, and model
    predictions. Additional functionality is included for computing and plotting
    posteriors.