adoptr.R util.R DataDistribution.R Prior.R PointMassPrior.R
ContinuousPrior.R TwoStageDesign.R GroupSequentialDesign.R
OneStageDesign.R Scores.R constraints.R minimize.R
ConditionalPower.R ConditionalSampleSize.R regularization.R
CompositeScore.R

Optimize one or two-arm, two-stage designs for clinical trials with
respect to several pre-implemented objective criteria or implement custom
objectives.
Optimization under uncertainty and conditional (given stage-one outcome)
constraints are supported.
See Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. A variational
approach to optimal two-stage designs. Statistics in Medicine. 2019;1–13.
<doi:10.1002/sim.8291> for details.

Optimize one or two-arm, two-stage designs for clinical trials with
respect to several pre-implemented objective criteria or implement custom
objectives.
Optimization under uncertainty and conditional (given stage-one outcome)
constraints are supported.
See Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. A variational
approach to optimal two-stage designs. Statistics in Medicine. 2019;38(21):4159–4171.
<doi:10.1002/sim.8291> for details.

knitr, rmarkdown, testthat, covr, rpact, vdiffr, pwr, dplyr,
ggplot2, tidyr, gridExtra, bookdown

c(
person("Kevin", "Kunzmann",
role = c("aut", "cre"),
email = "kevin.kunzmann@mrc-bsu.cam.ac.uk",
comment = c(ORCID = "0000-0002-1140-7143")
),
person("Maximilian", "Pilz",
role = c("aut"),
email = "pilz@imbi.uni-heidelberg.de",
comment = c(ORCID = "0000-0002-9685-1613")
)
)
adoptr.R util.R DataDistribution.R BinomialDistribution.R
NormalDistribution.R StudentDistribution.R Prior.R
PointMassPrior.R ContinuousPrior.R TwoStageDesign.R
GroupSequentialDesign.R OneStageDesign.R Scores.R constraints.R
minimize.R ConditionalPower.R ConditionalSampleSize.R
regularization.R CompositeScore.R MaximumSampleSize.R
Kevin Kunzmann [aut, cre] (<https://orcid.org/0000-0002-1140-7143>),
Maximilian Pilz [aut] (<https://orcid.org/0000-0002-9685-1613>)

Optimize one or two-arm, two-stage designs for clinical trials with
respect to several pre-implemented objective criteria or implement custom
objectives.
Optimization under uncertainty and conditional (given stage-one outcome)
constraints are supported.
See <doi:10.1002/sim.8291> and <doi:10.18637/jss.v098.i09> for details.