Man pages for kkmann/adoptr
Adaptive Optimal Two-Stage Designs in R

adoptr	Adaptive Optimal Two-Stage Designs
AverageN2-class	Regularization via L1 norm
BinomialDataDistribution-class	Binomial data distribution
boundary-designs	Boundary designs
bounds	Get support of a prior or data distribution
composite	Score Composition
condition	Condition a prior on an interval
ConditionalPower-class	(Conditional) Power of a Design
ConditionalSampleSize-class	(Conditional) Sample Size of a Design
Constraints	Formulating Constraints
ContinuousPrior-class	Continuous univariate prior distributions
critical-values	Query critical values of a design
cumulative_distribution_function	Cumulative distribution function
DataDistribution-class	Data distributions
expectation	Expected value of a function
get_initial_design	Initial design
GroupSequentialDesign-class	Group-sequential two-stage designs
make_tunable	Fix parameters during optimization
MaximumSampleSize-class	Maximum Sample Size of a Design
minimize	Find optimal two-stage design by constraint minimization
n	Query sample size of a design
N1-class	Regularize n1
NormalDataDistribution-class	Normal data distribution
OneStageDesign-class	One-stage designs
plot-TwoStageDesign-method	Plot 'TwoStageDesign' with optional set of conditional scores
PointMassPrior-class	Univariate discrete point mass priors
posterior	Compute posterior distribution
predictive_cdf	Predictive CDF
predictive_pdf	Predictive PDF
print.adoptrOptimizationResult	Printing an optimization result
Prior-class	Univariate prior on model parameter
probability_density_function	Probability density function
Scores	Scores
simulate-TwoStageDesign-numeric-method	Draw samples from a two-stage design
StudentDataDistribution-class	Student's t data distribution
subject_to	Create a collection of constraints
tunable_parameters	Switch between numeric and S4 class representation of a...
TwoStageDesign-class	Two-stage designs