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-class DataDistribution

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-class Prior

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-package 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()

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