Bayesian Progressive Three State Model with Censoring Due to Intervention R package BayesTSM version 1.0.1 In screening programs, individuals are usually followed up and tested (screened) for the development of a disease, such as cancer. The target disease often develops progressively in stages; for example healthy (state 1), pre-state disease (state 2), and the disease state (state 3). When the pre-state disease is found during screening it is intervened upon, for example by surgical removal of a lesion, so that the progression of the pre-state disease to disease is interrupted. This is called censoring due to intervention. Researchers often want to estimate the time from baseline to the pre-state disease, the time from the pre-state disease to the disease, and the total time from baseline to the disease. In addition, researchers often want to regress these times on baseline covariates. To these ends, 'BayesTSM' estimates a progressive three-state model with censoring due to intervention using Bayesian estimation methods, as described in Klausch et al. (2023) <doi:10.1214/22-AOAS1669>. Software Depends: R (>= 3.5.0) Imports: actuar, coda, doParallel, foreach, graphics, ggplot2, MASS, MCMCpack, mvtnorm, parallel, posterior, Rcpp, rlang, survival LinkingTo: Rcpp Suggests: knitr, rmarkdown, bookdown Thomas Klausch <t.klausch@amsterdamumc.nl> Comprehensive R Archive Network (CRAN) Thomas Klausch [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=BayesTSM/LICENSE) 2026-06-16 application/tgz https://CRAN.R-project.org/package=BayesTSM doi:10.32614/CRAN.package.BayesTSM