Adaptive Optimal Two-Stage Designs

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Documentation for package ‘adoptr’ version 1.0.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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

adoptr-package

Adaptive Optimal Two-Stage Designs

-- 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