BayesPIM: Bayesian Prevalence-Incidence Mixture Model

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 T. Klausch, B. I. Lissenberg-Witte, and V. M. Coupe (2024), "A Bayesian prevalence-incidence mixture model for screening outcomes with misclassification", <doi:10.48550/arXiv.2412.16065>.

Version:	1.0.0
Depends:	R (≥ 3.5.0), coda
Imports:	Rcpp, mvtnorm, MASS, ggamma, doParallel, foreach, parallel, actuar
LinkingTo:	Rcpp
Suggests:	knitr, rmarkdown
Published:	2025-03-22
DOI:	10.32614/CRAN.package.BayesPIM
Author:	Thomas Klausch [aut, cre]
Maintainer:	Thomas Klausch <t.klausch at amsterdamumc.nl>
BugReports:	https://github.com/thomasklausch2/BayesPIM/issues
License:	MIT + file LICENSE
URL:	https://github.com/thomasklausch2/bayespim
NeedsCompilation:	yes
Materials:	README
CRAN checks:	BayesPIM results

Documentation:

Reference manual:	BayesPIM.html , BayesPIM.pdf
Vignettes:	Introduction to BayesPIM (source, R code)

Downloads:

Package source:	BayesPIM_1.0.0.tar.gz
Windows binaries:	r-devel: BayesPIM_1.0.0.zip, r-release: BayesPIM_1.0.0.zip, r-oldrel: BayesPIM_1.0.0.zip
macOS binaries:	r-release (arm64): BayesPIM_1.0.0.tgz, r-oldrel (arm64): BayesPIM_1.0.0.tgz, r-release (x86_64): BayesPIM_1.0.0.tgz, r-oldrel (x86_64): BayesPIM_1.0.0.tgz

Linking:

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