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>.
| Version: | 0.1.0 |
| Depends: | R (≥ 4.1.0) |
| Imports: | dplyr, magrittr, methods, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), utils, furrr, future, readr, tibble, tidyr, reshape2 |
| LinkingTo: | BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0) |
| Published: | 2026-02-12 |
| DOI: | 10.32614/CRAN.package.BayesPET |
| Author: | Xinyi He [cre, aut], Jingyan Fu [aut], Ying Yuan [aut, cph] |
| Maintainer: | Xinyi He <xinyi.he at uth.tmc.edu> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| Materials: | NEWS |
| CRAN checks: | BayesPET results |
| Reference manual: | BayesPET.html , BayesPET.pdf |
| Package source: | BayesPET_0.1.0.tar.gz |
| Windows binaries: | r-devel: BayesPET_0.1.0.zip, r-release: BayesPET_0.1.0.zip, r-oldrel: BayesPET_0.1.0.zip |
| macOS binaries: | r-release (arm64): BayesPET_0.1.0.tgz, r-oldrel (arm64): BayesPET_0.1.0.tgz, r-release (x86_64): BayesPET_0.1.0.tgz, r-oldrel (x86_64): BayesPET_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=BayesPET to link to this page.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.