Function reference • adoptr

All functions
`AverageN2()` `evaluate(<AverageN2>,<TwoStageDesign>)`	Regularization via L1 norm
`Binomial()` `quantile(<Binomial>)` `simulate(<Binomial>,<numeric>)`	Binomial data distribution
`ConditionalPower()` `Power()` `evaluate(<ConditionalPower>,<TwoStageDesign>)`	(Conditional) Power of a Design
`ConditionalSampleSize()` `ExpectedSampleSize()` `evaluate(<ConditionalSampleSize>,<TwoStageDesign>)`	(Conditional) Sample Size of a Design
`evaluate(<Constraint>,<TwoStageDesign>)` `<=`(<ConditionalScore>,<numeric>) `>=`(<ConditionalScore>,<numeric>) `<=`(<numeric>,<ConditionalScore>) `>=`(<numeric>,<ConditionalScore>) `<=`(<ConditionalScore>,<ConditionalScore>) `>=`(<ConditionalScore>,<ConditionalScore>) `<=`(<UnconditionalScore>,<numeric>) `>=`(<UnconditionalScore>,<numeric>) `<=`(<numeric>,<UnconditionalScore>) `>=`(<numeric>,<UnconditionalScore>) `<=`(<UnconditionalScore>,<UnconditionalScore>) `>=`(<UnconditionalScore>,<UnconditionalScore>)	Formulating Constraints
`ContinuousPrior()`	Continuous univariate prior distributions
`DataDistribution-class` `DataDistribution`	Data distributions
`GroupSequentialDesign()` `TwoStageDesign(<GroupSequentialDesign>)`	Group-sequential two-stage designs
`MaximumSampleSize()` `evaluate(<MaximumSampleSize>,<TwoStageDesign>)`	Maximum Sample Size of a Design
`N1()` `evaluate(<N1>,<TwoStageDesign>)`	Regularize n1
`Normal()` `quantile(<Normal>)` `simulate(<Normal>,<numeric>)`	Normal data distribution
`OneStageDesign()` `TwoStageDesign(<OneStageDesign>)` `plot(<OneStageDesign>)`	One-stage designs
`PointMassPrior()`	Univariate discrete point mass priors
`Prior-class` `Prior`	Univariate prior on model parameter
`expected()` `evaluate()`	Scores
`Student()` `quantile(<Student>)` `simulate(<Student>,<numeric>)`	Student's t data distribution
`TwoStageDesign()` `summary(<TwoStageDesign>)`	Two-stage designs
`adoptr`	Adaptive Optimal Two-Stage Designs
`get_lower_boundary_design()` `get_upper_boundary_design()`	Boundary designs
`bounds()`	Get support of a prior or data distribution
`composite()` `evaluate(<CompositeScore>,<TwoStageDesign>)`	Score Composition
`condition()`	Condition a prior on an interval
`c2()`	Query critical values of a design
`cumulative_distribution_function()`	Cumulative distribution function
`expectation` `expectation,PointMassPrior,function-method` `expectation,ContinuousPrior,function-method`	Expected value of a function
`get_initial_design()`	Initial design
`make_tunable()` `make_fixed()`	Fix parameters during optimization
`minimize()`	Find optimal two-stage design by constraint minimization
`n1()` `n2()` `n()`	Query sample size of a design
`plot(<TwoStageDesign>)`	Plot `TwoStageDesign` with optional set of conditional scores
`posterior()`	Compute posterior distribution
`predictive_cdf()`	Predictive CDF
`predictive_pdf()`	Predictive PDF
`print()`	Printing an optimization result
`probability_density_function()`	Probability density function
`simulate(<TwoStageDesign>,<numeric>)`	Draw samples from a two-stage design
`subject_to()` `evaluate(<ConstraintsCollection>,<TwoStageDesign>)`	Create a collection of constraints
`tunable_parameters()` `update(<TwoStageDesign>)` `update(<OneStageDesign>)`	Switch between numeric and S4 class representation of a design

Reference

All functions