Adaptive Optimal Two-Stage Designs in R


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Documentation for package ‘adoptr’ version 1.0.0

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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