adestr1.0.0 package

Estimation in Optimal Adaptive Two-Stage Designs

adestr

adestr

NormalPrior

Normal prior distribution for the parameter mu

analyze

Analyze a dataset

c-EstimatorScoreResult-method

Combine EstimatoreScoreResult objects into a list

c-EstimatorScoreResultList-method

Combine EstimatoreScoreResult objects into a list

c2_extrapol

Calculate the second-stage critical value for a design with cached spl...

EstimatorScore-class

Performance scores for point and interval estimators

evaluate_estimator-methods

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 pap...

IntervalEstimator-class

Interval estimators

n2_extrapol

Calculate the second-stage sample size for a design with cached spline...

plot_p

Plot p-values and implied rejection boundaries

plot-EstimatorScoreResult-method

Plot performance scores for point and interval estimators

plot-EstimatorScoreResultList-method

Plot performance scores for point and interval estimators

plot-list-method

Plot performance scores for point and interval estimators

PointEstimator-class

Point estimators

PValue-class

P-values

Statistic-class

Statistics and Estimators of the adestr package

TwoStageDesignWithCache

TwoStageDesignWithCache constructor function

UniformPrior

Uniform prior distribution for the parameter mu

Methods to evaluate the performance characteristics of various point and interval estimators for optimal adaptive two-stage designs as described in Meis et al. (2024) <doi:10.1002/sim.10020>. Specifically, this package is written to work with trial designs created by the 'adoptr' package (Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09>; Pilz et al. (2021) <doi:10.1002/sim.8953>)). Apart from the a priori evaluation of performance characteristics, this package also allows for the evaluation of the implemented estimators on real datasets, and it implements methods to calculate p-values.