Title: Optimisation of Bayesian Decision Problems
Author: Sebastian Jobj�rnsson [aut, cre]
Description: Optimisation of the expected utility in single-stage and multi-stage Bayesian decision problems. The expected utility is estimated by simulation. For single-stage problems, JAGS is used to draw MCMC samples.

Title: Inference and design for predictive values in binary diagnostic
Description: Computation of asymptotic confidence intervals for
negative and positive predictive values in binary diagnostic
tests. Experimental design based for hypothesis tests on

Authors@R: c(person("Frank", "Schaarschmidt", role = c("aut", "cre"),
Description: Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values.
Packaged: 2014-03-06 10:10:45 UTC; Schaarschmidt

Title: Inference and Design for Predictive Values in Diagnostic Tests
Packaged: 2019-03-11 14:54:20 UTC; Schaarschmidt

Authors@R: c(person("Birgir", "Hrafnkelsson", email = "birgirhr@hi.is", role = c("aut","cph"),comment=c(ORCID="0000-0003-1864-9652")),
person("Solvi", "Rognvaldsson", email = "solviro@gmail.com", role = c("aut","cre"),comment=c(ORCID="0000-0002-4376-3361")),
person("Axel Orn", "Jansson", email = "axelorn94@gmail.com", role = c("aut")),
person("Rafael", "Vias", email = "raffidv@gmail.com", role = c("aut"))
Description: Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020) <arXiv:2010.04769>.
Imports: ggplot2, grid, gridExtra, rlang, scales
Suggests: testthat, knitr, rmarkdown, covr, vdiffr
Solvi Rognvaldsson [aut, cre] (<https://orcid.org/0000-0002-4376-3361>),

Title: Bayesian Detection of Potential Risk Using Inference on Blinded
role = c("aut", "cre"), email = "saurabh.mukhopadhyay@abbvie.com")
Description: Implements Bayesian inference to detect signal from blinded
clinical trial when total number of adverse events of special
concerns and total risk exposures from all patients are available in the study.
For more details see the article by Mukhopadhyay et. al. (2018)
titled 'Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data',
in Pharmaceutical Statistics (to appear).
Note: Package 'rjags' requires JAGS executabe file (compatiable