Inference and Design for Predictive Values in Diagnostic Tests

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"))
)
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>.
testthat, knitr, rmarkdown, covr, vdiffr
Birgir Hrafnkelsson [aut, cph]
(<https://orcid.org/0000-0003-1864-9652>),
Solvi Rognvaldsson [aut, cre] (<https://orcid.org/0000-0002-4376-3361>),
Axel Orn Jansson [aut],
Rafael Vias [aut]

Bayesian Detection of Potential Risk Using Inference on Blinded
Safety Data
person("Saurabh", "Mukhopadhyay",
role = c("aut", "cre"), email = "saurabh.mukhopadhyay@abbvie.com")
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).
Package 'rjags' requires JAGS executabe file (compatiable
version) should be already installed.

person("Markus Peter", "Auer", role = c("aut", "cre"),
email = "mp.auer@meanerreversion.com")
Beta drift poses a serious challenge to asset managers
and financial researchers. Beta drift causes problems in asset
pricing models and can have serious ramifications for hedging
attempts. Providing users with a tool that allows them to
quantify beta drift and form educated opinions about it is
the primary purpose of this package.
This package contains the BDA() function that performs a beta
drift analysis, typically for multi-factor asset pricing models.
The BDA() function tests the underlying model parameters for
drift across time, drift across model horizon, and applies a
jackknife procedure to the baseline model. This allows the users
to draw conclusions about the stability of model parameters or
make inferences about the behavior of funds. For example, the
drift of parameters for active funds could be interpreted as
implicit style drift or, in the case of passive funds, management's
inability to track a benchmark completely.