Package index

All functions

ANOVA()get_tau_ANOVA()

Analysis of Variance

AverageN2()evaluate(<AverageN2>,<TwoStageDesign>)

Regularization via L1 norm

Binomial()quantile(<Binomial>)simulate(<Binomial>,<numeric>)

Binomial data distribution

ChiSquared()quantile(<ChiSquared>)simulate(<ChiSquared>,<numeric>)

Chi-Squared data distribution

ConditionalPower()Power()evaluate(<ConditionalPower>,<TwoStageDesign>)

(Conditional) Power of a Design

ConditionalSampleSize()ExpectedSampleSize()ExpectedNumberOfEvents()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-classDataDistribution

Data distributions

GroupSequentialDesign()TwoStageDesign(<GroupSequentialDesign>)TwoStageDesign(<GroupSequentialDesignSurvival>)

Group-sequential two-stage designs

GroupSequentialDesignSurvival-class

Group-sequential two-stage designs for time-to-event-endpoints

MaximumSampleSize()evaluate(<MaximumSampleSize>,<TwoStageDesign>)

Maximum Sample Size of a Design

N1()evaluate(<N1>,<TwoStageDesign>)

Regularize n1

NestedModels()quantile(<NestedModels>)simulate(<NestedModels>,<numeric>)

F-Distribution

Normal()quantile(<Normal>)simulate(<Normal>,<numeric>)

Normal data distribution

OneStageDesign()TwoStageDesign(<OneStageDesign>)TwoStageDesign(<OneStageDesignSurvival>)plot(<OneStageDesign>)

One-stage designs

OneStageDesignSurvival-class

One-stage designs for time-to-event endpoints

Pearson2xK()get_tau_Pearson2xK()

Pearson's chi-squared test for contingency tables

PointMassPrior()

Univariate discrete point mass priors

Prior-classPrior

Univariate prior on model parameter

expected()evaluate()

Scores

Student()quantile(<Student>)simulate(<Student>,<numeric>)

Student's t data distribution

Survival()quantile(<Survival>)simulate(<Survival>,<numeric>)

Log-rank test

SurvivalDesign()TwoStageDesign(<TwoStageDesign>)OneStageDesign(<OneStageDesign>)GroupSequentialDesign(<GroupSequentialDesign>)

SurvivalDesign

TwoStageDesign()summary(<TwoStageDesign>)

Two-stage designs

TwoStageDesignSurvival-class

Two-stage design for time-to-event-endpoints

ZSquared()get_tau_ZSquared()

Distribution class of a squared normal distribution

adoptr-packageadoptr

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()

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>)

PlotTwoStageDesign 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