c(person(given = "Lukas",
family = "Baumann",
role = c("aut", "cre"),
email = "baumann@imbi.uni-heidelberg.de",
comment = c(ORCID = "0000-0001-7931-7470")),
person(given = "Maximilian",
family = "Pilz",
role = c("aut"),
email = "pilz@imbi.uni-heidelberg.de",
comment = c(ORCID = "0000-0002-9685-1613")),
person(given = "Institute of Medical Biometry and Informatics - University of Heidelberg",
role = c("cph")))
Lukas Baumann [aut, cre] (<https://orcid.org/0000-0001-7931-7470>),
Maximilian Pilz [aut] (<https://orcid.org/0000-0002-9685-1613>),
Institute of Medical Biometry and Informatics - University of
Heidelberg [cph]

c(person(given = "Lukas",
family = "Baumann",
role = c("aut", "cre"),
email = "baumann@imbi.uni-heidelberg.de",
comment = c(ORCID = "0000-0001-7931-7470")),
person(given = "Maximilian",
family = "Pilz",
role = c("aut"),
email = "maximilian.pilz@itwm.fraunhofer.de",
comment = c(ORCID = "0000-0002-9685-1613")),
person(given = "Institute of Medical Biometry and Informatics - University of Heidelberg",
role = c("cph")))
Computation of key characteristics and plots for blinded sample size recalculation.
Continuous as well as binary endpoints are supported in superiority and non-inferiority trials.
See Baumann, Pilz, Kieser (2022) <doi:10.32614/RJ-2022-001> for a detailed description.
The implemented methods include the approaches by
Lu, K. (2019) <doi:10.1002/pst.1737>,
Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>,
Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>,
Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and
Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.

Record Linkage for Empirically Motivated Priors
person("Rebecca", "Steorts", email = "beka@stat.duke.edu",
role = c("aut", "cre"))
R (>= 3.2.4), stringdist, RecordLinkage, plyr
An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette.