Package: adestr 1.0.0
adestr: Estimation in Optimal Adaptive Two-Stage Designs
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.
Authors:
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adestr.pdf |adestr.html✨
adestr/json (API)
NEWS
| # Install 'adestr' in R: |
| install.packages('adestr', repos = c('https://jan-imbi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jan-imbi/adestr/issues
adaptiveadoptrconfidencedesignsestimationintervalsoptimalparameterpointtwo-stage
Last updated 4 months agofrom:0df8d7d16b. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 09 2024 |
| R-4.5-win-x86_64 | OK | Nov 09 2024 |
| R-4.5-linux-x86_64 | OK | Nov 09 2024 |
| R-4.4-win-x86_64 | OK | Nov 09 2024 |
| R-4.4-mac-x86_64 | OK | Nov 09 2024 |
| R-4.4-mac-aarch64 | OK | Nov 09 2024 |
| R-4.3-win-x86_64 | OK | Nov 09 2024 |
| R-4.3-mac-x86_64 | OK | Nov 09 2024 |
| R-4.3-mac-aarch64 | OK | Nov 09 2024 |
Exports:AdaptivelyWeightedSampleMeananalyzeBiasBiasReducedCentralityCoverageevaluate_estimatorevaluate_scenarios_parallelExpectationFirstStageSampleMeanget_example_designget_example_statisticsget_stagewise_estimatorsIntervalEstimatorLikelihoodRatioOrderingCILikelihoodRatioOrderingPValueLinearShiftRepeatedPValueMedianUnbiasedLikelihoodRatioOrderingMedianUnbiasedMLEOrderingMedianUnbiasedNeymanPearsonOrderingMedianUnbiasedScoreTestOrderingMedianUnbiasedStagewiseCombinationFunctionOrderingMidpointLikelihoodRatioOrderingCIMidpointMLEOrderingCIMidpointNeymanPearsonOrderingCIMidpointScoreTestOrderingCIMidpointStagewiseCombinationFunctionOrderingCIMinimizePeakVarianceMLEOrderingCIMLEOrderingPValueMSENaiveCINaivePValueNeymanPearsonOrderingCINeymanPearsonOrderingPValueNormalPriorOverestimationProbabilityplotplot_pPointEstimatorPseudoRaoBlackwellPValueRaoBlackwellRepeatedCISampleMeanScoreTestOrderingCIScoreTestOrderingPValueSoftCoverageStagewiseCombinationFunctionOrderingCIStagewiseCombinationFunctionOrderingPValueTestAgreementUniformPriorVarianceWeightedSampleMeanWidth
Dependencies:abindadoptrbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11cubatureDerivdigestdoBydplyrfansifarverforcatsFormulafuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifglobalsgluegridExtragtableisobandlabelinglatex2explatticelifecyclelistenvlme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigpolynomprogressrpurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| adestr | adestr-package adestr |
| Analyze a dataset | analyze analyze,data.frame-method |
| Combine EstimatoreScoreResult objects into a list | c,EstimatorScoreResult-method |
| Combine EstimatoreScoreResult objects into a list | c,EstimatorScoreResultList-method |
| Calculate the second-stage critical value for a design with cached spline parameters | c2_extrapol |
| Performance scores for point and interval estimators | Bias Centrality Coverage EstimatorScore EstimatorScore-class Expectation MSE OverestimationProbability SoftCoverage TestAgreement Variance Width |
| Evaluate performance characteristics of an estimator | evaluate_estimator |
| Evaluate performance characteristics of an estimator | evaluate_estimator,Bias,PointEstimator-method evaluate_estimator,Centrality,PointEstimator-method evaluate_estimator,Coverage,IntervalEstimator-method evaluate_estimator,Expectation,PointEstimator-method evaluate_estimator,IntervalEstimatorScore,PointEstimator-method evaluate_estimator,list,Estimator-method evaluate_estimator,MSE,PointEstimator-method evaluate_estimator,OverestimationProbability,PointEstimator-method evaluate_estimator,PointEstimatorScore,IntervalEstimator-method evaluate_estimator,SoftCoverage,IntervalEstimator-method evaluate_estimator,TestAgreement,IntervalEstimator-method evaluate_estimator,TestAgreement,PValue-method evaluate_estimator,Variance,PointEstimator-method evaluate_estimator,Width,IntervalEstimator-method evaluate_estimator-methods |
| Evaluate different scenarios in parallel | evaluate_scenarios_parallel |
| Generate an exemplary adaptive design | get_example_design |
| Generate a list of estimators and p-values to use in examples | get_example_statistics |
| Conditional representations of an estimator or p-value | get_stagewise_estimators get_stagewise_estimators,AdaptivelyWeightedSampleMean,Normal-method get_stagewise_estimators,BiasReduced,Normal-method get_stagewise_estimators,IntervalEstimator,DataDistribution-method get_stagewise_estimators,IntervalEstimator,Student-method get_stagewise_estimators,LikelihoodRatioOrderingCI,Normal-method get_stagewise_estimators,LikelihoodRatioOrderingPValue,Normal-method get_stagewise_estimators,LinearShiftRepeatedPValue,Normal-method get_stagewise_estimators,MedianUnbiasedLikelihoodRatioOrdering,Normal-method get_stagewise_estimators,MedianUnbiasedMLEOrdering,Normal-method get_stagewise_estimators,MedianUnbiasedNeymanPearsonOrdering,Normal-method get_stagewise_estimators,MedianUnbiasedScoreTestOrdering,Normal-method get_stagewise_estimators,MedianUnbiasedStagewiseCombinationFunctionOrdering,Normal-method get_stagewise_estimators,MidpointLikelihoodRatioOrderingCI,Normal-method get_stagewise_estimators,MidpointMLEOrderingCI,Normal-method get_stagewise_estimators,MidpointNeymanPearsonOrderingCI,Normal-method get_stagewise_estimators,MidpointScoreTestOrderingCI,Normal-method get_stagewise_estimators,MidpointStagewiseCombinationFunctionOrderingCI,Normal-method get_stagewise_estimators,MinimizePeakVariance,Normal-method get_stagewise_estimators,MLEOrderingCI,Normal-method get_stagewise_estimators,MLEOrderingPValue,Normal-method get_stagewise_estimators,NaiveCI,Normal-method get_stagewise_estimators,NaivePValue,Normal-method get_stagewise_estimators,NeymanPearsonOrderingCI,Normal-method get_stagewise_estimators,NeymanPearsonOrderingPValue,Normal-method get_stagewise_estimators,PointEstimator,DataDistribution-method get_stagewise_estimators,PointEstimator,Student-method get_stagewise_estimators,PseudoRaoBlackwell,Normal-method get_stagewise_estimators,PValue,DataDistribution-method get_stagewise_estimators,PValue,Student-method get_stagewise_estimators,RaoBlackwell,Normal-method get_stagewise_estimators,RepeatedCI,Normal-method get_stagewise_estimators,ScoreTestOrderingCI,Normal-method get_stagewise_estimators,ScoreTestOrderingPValue,Normal-method get_stagewise_estimators,StagewiseCombinationFunctionOrderingCI,Normal-method get_stagewise_estimators,StagewiseCombinationFunctionOrderingPValue,Normal-method get_stagewise_estimators,VirtualIntervalEstimator,ANY-method get_stagewise_estimators,VirtualIntervalEstimator,Student-method get_stagewise_estimators,VirtualPointEstimator,ANY-method get_stagewise_estimators,VirtualPointEstimator,Student-method get_stagewise_estimators,VirtualPValue,ANY-method get_stagewise_estimators,VirtualPValue,Student-method |
| Generate the list of estimators and p-values that were used in the paper | get_statistics_from_paper |
| Interval estimators | ConfidenceInterval ConfidenceInterval-class IntervalEstimator IntervalEstimator-class LikelihoodRatioOrderingCI MLEOrderingCI NaiveCI NeymanPearsonOrderingCI RepeatedCI ScoreTestOrderingCI StagewiseCombinationFunctionOrderingCI |
| Calculate the second-stage sample size for a design with cached spline parameters | n2_extrapol |
| Normal prior distribution for the parameter mu | NormalPrior |
| Plot p-values and implied rejection boundaries | plot_p |
| Plot performance scores for point and interval estimators | 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 |
| Point estimators | AdaptivelyWeightedSampleMean BiasReduced FirstStageSampleMean MedianUnbiasedLikelihoodRatioOrdering MedianUnbiasedMLEOrdering MedianUnbiasedNeymanPearsonOrdering MedianUnbiasedScoreTestOrdering MedianUnbiasedStagewiseCombinationFunctionOrdering MidpointLikelihoodRatioOrderingCI MidpointMLEOrderingCI MidpointNeymanPearsonOrderingCI MidpointScoreTestOrderingCI MidpointStagewiseCombinationFunctionOrderingCI MinimizePeakVariance PointEstimator PointEstimator-class PseudoRaoBlackwell RaoBlackwell SampleMean WeightedSampleMean |
| P-values | LikelihoodRatioOrderingPValue LinearShiftRepeatedPValue MLEOrderingPValue NaivePValue NeymanPearsonOrderingPValue PValue PValue-class ScoreTestOrderingPValue StagewiseCombinationFunctionOrderingPValue |
| Statistics and Estimators of the adestr package | Estimator Statistic Statistic-class Statistics |
| TwoStageDesignWithCache constructor function | TwoStageDesignWithCache |
| Uniform prior distribution for the parameter mu | UniformPrior |
