The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

BayesPIM: Bayesian prevalence-incidence mixture model

BayesPIM is a versatile modeling environment for screening and surveillance data, as described in detail in the main reference Klausch et al. (2024). It is a so-called prevalence-incidence mixture (PIM) model with a Bayesian Gibbs sampler as the estimation backend. For details on usage see the vignette("BayesPIM_intro").

Installation

You can install the development version of BayesPIM from GitHub with:

# install.packages("devtools")
devtools::install_github("thomasklausch2/BayesPIM", build_vignettes = TRUE)

Example

This is a basic example of data generation and model fitting.

library(BayesPIM)

# Generate data according to the Klausch et al. (2024) PIM
set.seed(2025)
dat = gen.dat(kappa = 0.7, n= 1e3, theta = 0.2,
              p = 1, p.discrete = 1,
              beta.X = c(0.2,0.2), beta.W = c(0.2,0.2),
              v.min = 20, v.max = 30, mean.rc = 80,
              sigma.X = 0.2, mu.X=5, dist.X = "weibull",
              prob.r  = 1)

# An initial model fit with fixed test sensitivity kappa (approx. 1-3 minutes, depending on machine)
mod = bayes.2S( Vobs = dat$Vobs,
                Z.X = dat$Z,
                Z.W = dat$Z,
                r= dat$r,
                kappa = 0.7,
                update.kappa = F,
                ndraws= 1e4,
                chains = 4,
                prop.sd.X = 0.008,
                parallel = T,
                dist.X = 'weibull'
)

# Inspect results
mod$runtime # runtime of Gibbs sampler
plot( trim.mcmc( mod$par.X.all, thining = 10) ) # MCMC chains including burn-in
plot( trim.mcmc( mod$par.X.bi, thining = 10) ) # MCMC chains excluding burn-in
apply(mod$ac.X, 2, mean) # Acceptance rates per chain
gelman.diag(mod$par.X.bi) # Gelman convergence diagnostics

References

T. Klausch, B. I. Lissenberg-Witte, and V. M. Coupe (2024). “A Bayesian prevalence-incidence mixture model for screening outcomes with misclassification.” arXiv:2412.16065. https://arxiv.org/abs/2412.16065

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.