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:Jan Meis [aut, cre], Martin Maechler [cph]

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manual.pdf |manual.html✨
card.svg |card.png
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

Pkgdown/docs site:https://jan-imbi.github.io

On CRAN:

Conda:

adaptiveadoptrconfidencedesignsestimationintervalsoptimalparameterpointtwo-stage

3.78 score 12 scripts 259 downloads 55 exports 89 dependencies

Last updated from:0df8d7d16b. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK196
linux-devel-x86_64OK205
source / vignettesOK234
linux-release-arm64OK194
linux-release-x86_64OK176
macos-release-arm64OK252
macos-release-x86_64OK361
macos-oldrel-arm64OK195
macos-oldrel-x86_64OK304
windows-develOK165
windows-releaseOK188
windows-oldrelOK158
wasm-releaseOK133

Exports:AdaptivelyWeightedSampleMeananalyzeBiasBiasReducedCentralityCoverageevaluate_estimatorevaluate_scenarios_parallelExpectationFirstStageSampleMeanget_example_designget_example_statisticsget_stagewise_estimatorsIntervalEstimatorLikelihoodRatioOrderingCILikelihoodRatioOrderingPValueLinearShiftRepeatedPValueMedianUnbiasedLikelihoodRatioOrderingMedianUnbiasedMLEOrderingMedianUnbiasedNeymanPearsonOrderingMedianUnbiasedScoreTestOrderingMedianUnbiasedStagewiseCombinationFunctionOrderingMidpointLikelihoodRatioOrderingCIMidpointMLEOrderingCIMidpointNeymanPearsonOrderingCIMidpointScoreTestOrderingCIMidpointStagewiseCombinationFunctionOrderingCIMinimizePeakVarianceMLEOrderingCIMLEOrderingPValueMSENaiveCINaivePValueNeymanPearsonOrderingCINeymanPearsonOrderingPValueNormalPriorOverestimationProbabilityplotplot_pPointEstimatorPseudoRaoBlackwellPValueRaoBlackwellRepeatedCISampleMeanScoreTestOrderingCIScoreTestOrderingPValueSoftCoverageStagewiseCombinationFunctionOrderingCIStagewiseCombinationFunctionOrderingPValueTestAgreementUniformPriorVarianceWeightedSampleMeanWidth

Dependencies:abindadoptrbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11cubatureDerivdigestdoBydplyrfarverforcatsforecastFormulafracdifffuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifglobalsgluegridExtragtableisobandlabelinglatex2explatticelifecyclelistenvlme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigpolynomprogressrpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrstatixS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

Introduction to adestr

Jan Meis

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2024-07-11
Started: 2023-04-06

Readme and manuals

Help Manual

Help pageTopics
adestradestr-package adestr
Analyze a datasetanalyze analyze,data.frame-method
Combine EstimatoreScoreResult objects into a listc,EstimatorScoreResult-method
Combine EstimatoreScoreResult objects into a listc,EstimatorScoreResultList-method
Calculate the second-stage critical value for a design with cached spline parametersc2_extrapol
Performance scores for point and interval estimatorsBias Centrality Coverage EstimatorScore EstimatorScore-class Expectation MSE OverestimationProbability SoftCoverage TestAgreement Variance Width
Evaluate performance characteristics of an estimatorevaluate_estimator
Evaluate performance characteristics of an estimatorevaluate_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 parallelevaluate_scenarios_parallel
Generate an exemplary adaptive designget_example_design
Generate a list of estimators and p-values to use in examplesget_example_statistics
Conditional representations of an estimator or p-valueget_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 paperget_statistics_from_paper
Interval estimatorsConfidenceInterval ConfidenceInterval-class IntervalEstimator IntervalEstimator-class LikelihoodRatioOrderingCI MLEOrderingCI NaiveCI NeymanPearsonOrderingCI RepeatedCI ScoreTestOrderingCI StagewiseCombinationFunctionOrderingCI
Calculate the second-stage sample size for a design with cached spline parametersn2_extrapol
Normal prior distribution for the parameter muNormalPrior
Plot p-values and implied rejection boundariesplot_p
Plot performance scores for point and interval estimatorsplot,EstimatorScoreResult-method
Plot performance scores for point and interval estimatorsplot,EstimatorScoreResultList-method
Plot performance scores for point and interval estimatorsplot,list-method
Point estimatorsAdaptivelyWeightedSampleMean BiasReduced FirstStageSampleMean MedianUnbiasedLikelihoodRatioOrdering MedianUnbiasedMLEOrdering MedianUnbiasedNeymanPearsonOrdering MedianUnbiasedScoreTestOrdering MedianUnbiasedStagewiseCombinationFunctionOrdering MidpointLikelihoodRatioOrderingCI MidpointMLEOrderingCI MidpointNeymanPearsonOrderingCI MidpointScoreTestOrderingCI MidpointStagewiseCombinationFunctionOrderingCI MinimizePeakVariance PointEstimator PointEstimator-class PseudoRaoBlackwell RaoBlackwell SampleMean WeightedSampleMean
P-valuesLikelihoodRatioOrderingPValue LinearShiftRepeatedPValue MLEOrderingPValue NaivePValue NeymanPearsonOrderingPValue PValue PValue-class ScoreTestOrderingPValue StagewiseCombinationFunctionOrderingPValue
Statistics and Estimators of the adestr packageEstimator Statistic Statistic-class Statistics
TwoStageDesignWithCache constructor functionTwoStageDesignWithCache
Uniform prior distribution for the parameter muUniformPrior