# Adaptive Optimal Two-Stage Designs in R

## Documentation for package ‘adoptr’ version 1.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A B C D E G M N O P Q S T U misc

## -- A --

adoptr | Adaptive Optimal Two-Stage Designs
AverageN2 | Regularization via L1 norm
AverageN2-class | Regularization via L1 norm

## -- B --

Binomial | Binomial data distribution
Binomial-class | Binomial data distribution
bounds | Get support of a prior or data distribution
bounds-method | Get support of a prior or data distribution

## -- C --

c2 | Query critical values of a design
c2-method | Query critical values of a design
composite | Score Composition
condition | Condition a prior on an interval
condition-method | Condition a prior on an interval
ConditionalPower | (Conditional) Power of a Design
ConditionalPower-class | (Conditional) Power of a Design
ConditionalSampleSize | (Conditional) Sample Size of a Design
ConditionalSampleSize-class | (Conditional) Sample Size of a Design
ConstraintCollection | Create a collection of constraints
Constraints | Formulating Constraints
ContinuousPrior | Continuous univariate prior distributions
ContinuousPrior-class | Continuous univariate prior distributions
cumulative_distribution_function | Cumulative distribution function
cumulative_distribution_function-method | Cumulative distribution function

## -- D --

DataDistribution | Data distributions
DataDistribution-class | Data distributions

## -- E --

evaluate | Scores
evaluate-method | Regularization via L1 norm
evaluate-method | (Conditional) Power of a Design
evaluate-method | (Conditional) Sample Size of a Design
evaluate-method | Formulating Constraints
evaluate-method | Maximum Sample Size of a Design
evaluate-method | Regularize n1
evaluate-method | Scores
evaluate-method | Score Composition
evaluate-method | Create a collection of constraints
expectation | Expected value of a function
expectation-method | Expected value of a function
expected | Scores
expected-method | Scores
ExpectedSampleSize | (Conditional) Sample Size of a Design

## -- G --

get_initial_design | Initial design
get_lower_boundary_design | Boundary designs
get_lower_boundary_design-method | Boundary designs
get_upper_boundary_design | Boundary designs
get_upper_boundary_design-method | Boundary designs
GroupSequentialDesign | Group-sequential two-stage designs
GroupSequentialDesign-class | Group-sequential two-stage designs

## -- M --

make_fixed | Fix parameters during optimization
make_fixed-method | Fix parameters during optimization
make_tunable | Fix parameters during optimization
make_tunable-method | Fix parameters during optimization
MaximumSampleSize | Maximum Sample Size of a Design
MaximumSampleSize-class | Maximum Sample Size of a Design
minimize | Find optimal two-stage design by constraint minimization

## -- N --

n | Query sample size of a design
n-method | Query sample size of a design
N1 | Regularize n1
n1 | Query sample size of a design
N1-class | Regularize n1
n1-method | Query sample size of a design
n2 | Query sample size of a design
n2-method | Query sample size of a design
Normal | Normal data distribution
Normal-class | Normal data distribution

## -- O --

OneStageDesign | One-stage designs
OneStageDesign-class | One-stage designs

## -- P --

plot-method | One-stage designs
plot-method | Plot 'TwoStageDesign' with optional set of conditional scores
PointMassPrior | Univariate discrete point mass priors
PointMassPrior-class | Univariate discrete point mass priors
posterior | Compute posterior distribution
posterior-method | Compute posterior distribution
Power | (Conditional) Power of a Design
predictive_cdf | Predictive CDF
predictive_cdf-method | Predictive CDF
predictive_pdf | Predictive PDF
predictive_pdf-method | Predictive PDF
print | Printing an optimization result
print.adoptrOptimizationResult | Printing an optimization result
Prior | Univariate prior on model parameter
Prior-class | Univariate prior on model parameter
probability_density_function | Probability density function
probability_density_function-method | Probability density function

## -- Q --

quantile-method | Binomial data distribution
quantile-method | Normal data distribution
quantile-method | Student's t data distribution

## -- S --

Scores | Scores
simulate-method | Binomial data distribution
simulate-method | Normal data distribution
simulate-method | Student's t data distribution
simulate-method | Draw samples from a two-stage design
Student | Student's t data distribution
Student-class | Student's t data distribution
subject_to | Create a collection of constraints
summary-method | Two-stage designs

## -- T --

tunable_parameters | Switch between numeric and S4 class representation of a design
tunable_parameters-method | Switch between numeric and S4 class representation of a design
TwoStageDesign | Two-stage designs
TwoStageDesign-class | Two-stage designs
TwoStageDesign-method | Group-sequential two-stage designs
TwoStageDesign-method | One-stage designs
TwoStageDesign-method | Two-stage designs

## -- U --

update-method | Switch between numeric and S4 class representation of a design

## -- misc --

<=-method | Formulating Constraints
>=-method | Formulating Constraints