Bayesian Meta-Analysis of diagnostic test data
Dr. Pablo Emilio Verde <pabloemilio.verde@uni-duesseldorf.de> &
Arnold Sykosch <arnold.sykosch@uni-duesseldorf.de>
Dr. Pablo Emilio Verde
<pabloemilio.verde@uni-duesseldorf.de>
Bayesian meta-analysis of diagnostic test data based on a
scale mixtures bivariate random-effects model. Summary
statistics are based on pooled and predictive sensitivities and
specificities. Model checking is used for diagnostic of
random-effects' misfit and predictive data is used to assess
models departures from observed data.

Bayesian Meta-Analysis of Diagnostic Test Data
c(person("Pablo Emilio", "Verde", role=c("aut","cre"),
email="pabloemilio.verde@hhu.de"))
JAGS (>= 3.4.0) (see
http://mcmc-jags.sourceforge.net)
Functions for Bayesian meta-analysis of diagnostic test data which are based on a scale mixtures bivariate random-effects model.

Functions for Bayesian mete-analysis of diagnostic test data which are based on a scale mixtures bivariate random-effects model.

rjags (>= 3.4.0), R2jags (>= 0.04-03), ggplot2, ggExtra, MASS,
grid, gridExtra
Functions for Bayesian meta-analysis of diagnostic test data which
are based on a scale mixtures bivariate random-effects model.

c(person("Pablo Emilio", "Verde", role=c("aut","cre"), email="pabloemilio.verde@hhu.de"))
rjags (>= 4.0.0), R2jags (>= 0.04-03), stats, ggplot2,
ggExtra, MASS, grid, gridExtra
JAGS (>= 4.3.0) (see
http://mcmc-jags.sourceforge.net)
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

Bayesian Additive Models for Location Scale and Shape (and
Beyond)
c(person(given = "Nikolaus", family = "Umlauf", role = c("aut", "cre"), email = "Nikolaus.Umlauf@uibk.ac.at"),
person(given = "Nadja", family = "Klein", role = "aut", email = "nklein@uni-goettingen.de"),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org"),
person(given = "Meike", family = "Koehler", role = "aut", email = "Meike.Koehler@helmholtz-muenchen.de"))
R infrastructures for Bayesian regression models.
Formula, MBA, mvtnorm, sp, spam, Matrix, survival, methods,
parallel
akima, bit, fields, gamlss, geoR, rjags, BayesX, BayesXsrc,
mapdata, maps, maptools, raster, spatstat, spdep, zoo
Nikolaus Umlauf [aut, cre],
Nadja Klein [aut],
Achim Zeileis [aut],
Meike Koehler [aut]