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

adoptr-package | Adaptive Optimal Two-Stage Designs |

adoptr | Adaptive Optimal Two-Stage Designs |

AverageN2 | Regularization via L1 norm |

AverageN2-class | Regularization via L1 norm |

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 |

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 |

DataDistribution | Data distributions |

DataDistribution-class | Data distributions |

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 |

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 |

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 | 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 |

OneStageDesign | One-stage designs |

OneStageDesign-class | One-stage designs |

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 |

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 |

quantile-method | Binomial data distribution |

quantile-method | Normal data distribution |

quantile-method | Student's t data distribution |

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 |

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 |

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

<=-method | Formulating Constraints |

>=-method | Formulating Constraints |