BayesPIM: Bayesian Prevalence-Incidence Mixture Model

Package: BayesPIM 1.0.1

Thomas Klausch

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 Klausch, Lissenberg-Witte and Coupé (2026) <doi:10.1002/sim.70433>.

Authors:Thomas Klausch [aut, cre]

BayesPIM_1.0.1.tar.gz
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BayesPIM_1.0.1.tgz(r-4.6-x86_64)BayesPIM_1.0.1.tgz(r-4.6-arm64)BayesPIM_1.0.1.tgz(r-4.5-x86_64)BayesPIM_1.0.1.tgz(r-4.5-arm64)
BayesPIM_1.0.1.tar.gz(r-4.7-arm64)BayesPIM_1.0.1.tar.gz(r-4.7-x86_64)BayesPIM_1.0.1.tar.gz(r-4.6-arm64)BayesPIM_1.0.1.tar.gz(r-4.6-x86_64)
BayesPIM_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
BayesPIM/json (API)

# Install 'BayesPIM' in R:

install.packages('BayesPIM', repos = c('https://thomasklausch2.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/thomasklausch2/bayespim/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

3.70 score 350 downloads 8 exports 12 dependencies

Last updated from:7ef8c0aa13. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	132
linux-devel-x86_64	OK	137
source / vignettes	OK	205
linux-release-arm64	OK	130
linux-release-x86_64	OK	128
macos-release-arm64	OK	171
macos-release-x86_64	OK	260
macos-oldrel-arm64	OK	190
macos-oldrel-x86_64	OK	186
windows-devel	OK	152
windows-release	OK	146
windows-oldrel	OK	118
wasm-release	OK	109

Exports:bayes.2S bayes.2S_seq gen.dat get.IC_2S get.ppd.2S search.prop.sd search.prop.sd_seq trim.mcmc

Dependencies:actuar coda codetools doParallel expint foreach ggamma iterators lattice MASS mvtnorm Rcpp

Introduction to BayesPIM

Rendered fromBayesPIM_intro.Rmdusingknitr::rmarkdown

Help page	Topics
Fitting Bayesian Prevalence-Incidence Mixture Model	bayes.2S
bayes.2S_seq: Bayesian Prevalence-Incidence Mixture Model (sequential processing)	bayes.2S_seq
gen.dat: Simulate Screening Data for a Prevalence-Incidence Mixture Model	gen.dat
Compute Information Criteria for a Bayesian Prevalence-Incidence Mixture Model	get.IC_2S
Posterior Predictive Cumulative Incidence Function	get.ppd.2S
Automated Heuristic Search of a Proposal Standard Deviation for 'bayes.2S'	search.prop.sd
Automated Heuristic Search of a Proposal Standard Deviation for 'bayes.2S' (sequential processing)	search.prop.sd_seq
Subset MCMC draws (burn-in and thinning)	trim.mcmc

Help page

Topics

Fitting Bayesian Prevalence-Incidence Mixture Model

bayes.2S

bayes.2S_seq: Bayesian Prevalence-Incidence Mixture Model (sequential processing)

bayes.2S_seq

gen.dat: Simulate Screening Data for a Prevalence-Incidence Mixture Model

gen.dat

Compute Information Criteria for a Bayesian Prevalence-Incidence Mixture Model

get.IC_2S

Posterior Predictive Cumulative Incidence Function

get.ppd.2S