Skip to content

bayesCureRateModel

1.6Package record

Bayesian Cure Rate Modeling for Time-to-Event Data

0packages depend
5.4Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

A fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2024) doi:10.1007/s11749-024-00942-w and Papastamoulis and Milienos (2024b) doi:10.48550/arXiv.2409.10221. The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution.

Install

Health

CRAN checks
13OK
Slowest check: 4.2 min · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-06-09
  • ERROR12 OK, 1 ERROR2026-06-08
  • OK14 OK2026-04-22
Show 2 earlier changesHide 2 earlier changes
  • ERROR13 OK, 1 ERROR2026-04-18
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 1.6
READMENoVignettesNoNEWSNoCitation fileYes · cites a publicationWebsitenot declared in DESCRIPTION
Help pages
26includes a package overview page
Examples that run
100%21 run in a plain check
Exported functions with examples
not tracked
Documented parameters
not tracked
Return-value docs
not tracked
Help pages citing references
58%

Downloads

5.4K
CRAN downloads in the past year
Rank #7,733 · ~15/day · ~451/mo
Daily download trend is not available in this view yet.
42430 days
1.2K90 days
5.4K1 year
Compare downloads with other packages →
Also on419 r2u38 autocran

Repository

1Stars
0Forks
0Open issues
0Open PRs
0Releases
47Commits
1Commit identities
47 commits · Last activity 2026-01-19
Responsiveness
not measured

Stars over time

2024-08-19 · 12026-07-07 · 1
PracticesBeta

2 development-tooling and community-health practices detected across 1 family in the upstream repository

Checks run against github.com/mqbssppe/bayesian_cure_rate_model on 2026-10-18.

Continuous integration (2)
GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies
11 external dependencies (excludes base and recommended)

Installing bayesCureRateModel adds 54 packages (32 with compiled code, 50 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.

Depends (1)
R >= 3.5.0
LinkingTo (2)
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (2)
Author, Maintainer since 1.0 (2024)
Releases (7)

Pick two releases to compare their code metrics.

Package metadata

First published
2024-06-27
Total releases
7 / 2 yrs
License
GPL-2 OSI
Minimum R
≥ 3.5.0
Bundled data
21 KB / 2 files
Download size
76 KB

Cite

Cite this package

Run in R for the authors' entry:

citation("bayesCureRateModel")

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.1007/s11749-024-00942-w.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for bayesCureRateModel version 1.6 [Data set]. HJJB, LLC. Data release v2026-10-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

Report a problem with this page →

Privacy choices

These apply to this browser and are stored on this device only. Nothing about your choice is sent to us.

Read the privacy policy