Function reference • adestr

All functions

Expectation() Bias() Variance() MSE() OverestimationProbability() Coverage() SoftCoverage() Width() TestAgreement() Centrality(): Performance scores for point and interval estimators

IntervalEstimator() RepeatedCI() StagewiseCombinationFunctionOrderingCI() MLEOrderingCI() LikelihoodRatioOrderingCI() ScoreTestOrderingCI() NeymanPearsonOrderingCI() NaiveCI(): Interval estimators

Normal(): Normally distributed data with known variance

NormalPrior(): Normal prior distribution for the parameter mu

PValue() LinearShiftRepeatedPValue() MLEOrderingPValue() LikelihoodRatioOrderingPValue() ScoreTestOrderingPValue() StagewiseCombinationFunctionOrderingPValue() NeymanPearsonOrderingPValue() NaivePValue(): P-values

PointEstimator() SampleMean() FirstStageSampleMean() WeightedSampleMean() AdaptivelyWeightedSampleMean() MinimizePeakVariance() BiasReduced() RaoBlackwell() PseudoRaoBlackwell() MidpointStagewiseCombinationFunctionOrderingCI() MidpointMLEOrderingCI() MidpointLikelihoodRatioOrderingCI() MidpointScoreTestOrderingCI() MidpointNeymanPearsonOrderingCI() MedianUnbiasedStagewiseCombinationFunctionOrdering() MedianUnbiasedMLEOrdering() MedianUnbiasedLikelihoodRatioOrdering() MedianUnbiasedScoreTestOrdering() MedianUnbiasedNeymanPearsonOrdering(): Point estimators

Statistic-class Statistic Statistics Estimator: Statistics and Estimators of the adestr package

Student(): Normally distributed data with unknown variance

TwoStageDesign-class: Re-export of two-stage design class

TwoStageDesignWithCache(): TwoStageDesignWithCache constructor function

UniformPrior(): Uniform prior distribution for the parameter mu

adestr adestr-package: adestr

analyze(): Analyze a dataset

c(<EstimatorScoreResult>): Combine EstimatoreScoreResult objects into a list

c(<EstimatorScoreResultList>): Combine EstimatoreScoreResult objects into a list

c2_extrapol(): Calculate the second-stage critical value for a design with cached spline parameters

evaluate_estimator(<PointEstimatorScore>,<IntervalEstimator>) evaluate_estimator(<IntervalEstimatorScore>,<PointEstimator>) evaluate_estimator(<list>,<Estimator>) evaluate_estimator(<Expectation>,<PointEstimator>) evaluate_estimator(<Bias>,<PointEstimator>) evaluate_estimator(<Variance>,<PointEstimator>) evaluate_estimator(<MSE>,<PointEstimator>) evaluate_estimator(<OverestimationProbability>,<PointEstimator>) evaluate_estimator(<Coverage>,<IntervalEstimator>) evaluate_estimator(<SoftCoverage>,<IntervalEstimator>) evaluate_estimator(<Width>,<IntervalEstimator>) evaluate_estimator(<TestAgreement>,<IntervalEstimator>) evaluate_estimator(<TestAgreement>,<PValue>) evaluate_estimator(<Centrality>,<PointEstimator>): Evaluate performance characteristics of an estimator

evaluate_estimator(): Evaluate performance characteristics of an estimator

evaluate_scenarios_parallel(): Evaluate different scenarios in parallel

get_example_design(): Generate an exemplary adaptive design

get_example_statistics(): Generate a list of estimators and p-values to use in examples

get_stagewise_estimators(): Conditional representations of an estimator or p-value

get_statistics_from_paper(): Generate the list of estimators and p-values that were used in the paper

n2_extrapol(): Calculate the second-stage sample size for a design with cached spline parameters

plot(<EstimatorScoreResult>): Plot performance scores for point and interval estimators

plot(<EstimatorScoreResultList>): Plot performance scores for point and interval estimators

plot(<list>): Plot performance scores for point and interval estimators

plot_p(): Plot p-values and implied rejection boundaries