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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")
.
You can install the development version of BayesPIM from GitHub with:
# install.packages("devtools")
::install_github("thomasklausch2/BayesPIM", build_vignettes = TRUE) devtools
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)
= gen.dat(kappa = 0.7, n= 1e3, theta = 0.2,
dat 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)
= bayes.2S( Vobs = dat$Vobs,
mod Z.X = dat$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
$runtime # runtime of Gibbs sampler
modplot( trim.mcmc( mod$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
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.
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