Package: adoptr 1.1.2.9000

Maximilian Pilz

adoptr: Adaptive Optimal Two-Stage Designs

Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.

Authors:Kevin Kunzmann [aut, cph], Maximilian Pilz [aut, cre], Jan Meis [aut], Nico Bruder [aut]

adoptr_1.1.2.9000.tar.gz
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adoptr_1.1.2.9000.tgz(r-4.6-any)adoptr_1.1.2.9000.tgz(r-4.5-any)
adoptr_1.1.2.9000.tar.gz(r-4.7-any)adoptr_1.1.2.9000.tar.gz(r-4.6-any)
adoptr_1.1.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
adoptr/json (API)
NEWS

# Install 'adoptr' in R:
install.packages('adoptr', repos = c('https://optad.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/optad/adoptr/issues

Pkgdown/docs site:https://optad.github.io

On CRAN:

Conda:

6.39 score 1 stars 1 packages 39 scripts 394 downloads 54 exports 2 dependencies

Last updated from:670580d15b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK147
source / vignettesOK311
linux-release-x86_64OK159
macos-release-arm64OK110
macos-oldrel-arm64OK135
windows-develOK118
windows-releaseOK106
windows-oldrelOK102
wasm-releaseOK111

Exports:ANOVAAverageN2Binomialboundsc2ChiSquaredcompositeconditionConditionalPowerConditionalSampleSizeContinuousPriorcumulative_distribution_functionevaluateexpectationexpectedExpectedNumberOfEventsExpectedSampleSizeget_initial_designget_lower_boundary_designget_tau_ANOVAget_tau_Pearson2xKget_tau_ZSquaredget_upper_boundary_designGroupSequentialDesignmake_fixedmake_tunableMaximumSampleSizeminimizenn1N1n2NestedModelsNormalOneStageDesignPearson2xKplotPointMassPriorposteriorPowerpredictive_cdfpredictive_pdfprobability_density_functionquantilesimulateStudentsubject_tosummarySurvivalSurvivalDesigntunable_parametersTwoStageDesignupdateZSquared

Dependencies:gluenloptr

Composite Scores

Rendered fromcomposite-scores.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2024-06-19
Started: 2024-06-06

Conditional Scores and Constraints

Rendered fromconditional-scores.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2024-06-06
Started: 2024-06-06

Defining New Scores

Rendered fromdefining-new-scores.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2024-06-06
Started: 2024-06-06

Designs for non-normal Endpoints with approximately normal test statistics

Rendered fromother-endpoints.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2024-07-23
Started: 2024-07-23

Get started with adoptr

Rendered fromadoptr.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2024-07-16
Started: 2024-06-06

The adoptr Package: Adaptive Optimal Designs for Clinical Trials in R

Kevin Kunzmann, Maximilian Pilz, Carolin Herrmann, Geraldine Rauch, Meinhard Kieser

Rendered fromadoptr_jss.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2026-05-02
Started: 2024-06-06

Working with priors

Rendered fromworking-with-priors.Rmdusingknitr::rmarkdownon May 06 2026.

Last update: 2024-06-06
Started: 2024-06-06

Readme and manuals

Help Manual

Help pageTopics
Adaptive Optimal Two-Stage Designsadoptr-package adoptr
Analysis of VarianceANOVA ANOVA-class get_tau_ANOVA
Regularization via L1 normAverageN2 AverageN2-class evaluate,AverageN2,TwoStageDesign-method
Binomial data distributionBinomial Binomial-class quantile,Binomial-method simulate,Binomial,numeric-method
Get support of a prior or data distributionbounds bounds,ContinuousPrior-method bounds,PointMassPrior-method
Query critical values of a designc2 c2,OneStageDesign,numeric-method c2,TwoStageDesign,numeric-method
Chi-Squared data distributionChiSquared ChiSquared-class quantile,ChiSquared-method simulate,ChiSquared,numeric-method
Score Compositioncomposite evaluate,CompositeScore,TwoStageDesign-method
Condition a prior on an intervalcondition condition,ContinuousPrior,numeric-method condition,PointMassPrior,numeric-method
(Conditional) Power of a DesignConditionalPower ConditionalPower-class evaluate,ConditionalPower,TwoStageDesign-method Power
(Conditional) Sample Size of a DesignConditionalSampleSize ConditionalSampleSize-class evaluate,ConditionalSampleSize,TwoStageDesign-method ExpectedNumberOfEvents ExpectedSampleSize
Formulating Constraints<=,ConditionalScore,ConditionalScore-method <=,ConditionalScore,numeric-method <=,numeric,ConditionalScore-method <=,numeric,UnconditionalScore-method <=,UnconditionalScore,numeric-method <=,UnconditionalScore,UnconditionalScore-method >=,ConditionalScore,ConditionalScore-method >=,ConditionalScore,numeric-method >=,numeric,ConditionalScore-method >=,numeric,UnconditionalScore-method >=,UnconditionalScore,numeric-method >=,UnconditionalScore,UnconditionalScore-method Constraints evaluate,Constraint,TwoStageDesign-method
Continuous univariate prior distributionsContinuousPrior ContinuousPrior-class
Cumulative distribution functioncumulative_distribution_function cumulative_distribution_function,Binomial,numeric,numeric,numeric-method cumulative_distribution_function,ChiSquared,numeric,numeric,numeric-method cumulative_distribution_function,NestedModels,numeric,numeric,numeric-method cumulative_distribution_function,Normal,numeric,numeric,numeric-method cumulative_distribution_function,Student,numeric,numeric,numeric-method cumulative_distribution_function,Survival,numeric,numeric,numeric-method
Data distributionsDataDistribution DataDistribution-class
Expected value of a functionexpectation expectation,ContinuousPrior,function-method expectation,PointMassPrior,function-method
Initial designget_initial_design
Boundary designsget_lower_boundary_design get_lower_boundary_design,GroupSequentialDesign-method get_lower_boundary_design,OneStageDesign-method get_lower_boundary_design,TwoStageDesign-method get_upper_boundary_design get_upper_boundary_design,GroupSequentialDesign-method get_upper_boundary_design,OneStageDesign-method get_upper_boundary_design,TwoStageDesign-method
Group-sequential two-stage designsGroupSequentialDesign GroupSequentialDesign,numeric-method GroupSequentialDesign-class TwoStageDesign,GroupSequentialDesign-method TwoStageDesign,GroupSequentialDesignSurvival-method
Group-sequential two-stage designs for time-to-event-endpointsGroupSequentialDesignSurvival-class
Fix parameters during optimizationmake_fixed make_fixed,TwoStageDesign-method make_tunable make_tunable,TwoStageDesign-method
Maximum Sample Size of a Designevaluate,MaximumSampleSize,TwoStageDesign-method MaximumSampleSize MaximumSampleSize-class
Find optimal two-stage design by constraint minimizationminimize
Query sample size of a designn n,TwoStageDesign,numeric-method n1 n1,TwoStageDesign-method n2 n2,GroupSequentialDesign,numeric-method n2,OneStageDesign,numeric-method n2,TwoStageDesign,numeric-method
Regularize n1evaluate,N1,TwoStageDesign-method N1 N1-class
F-DistributionNestedModels NestedModels-class quantile,NestedModels-method simulate,NestedModels,numeric-method
Normal data distributionNormal Normal-class quantile,Normal-method simulate,Normal,numeric-method
One-stage designsOneStageDesign OneStageDesign,numeric-method OneStageDesign-class plot,OneStageDesign-method TwoStageDesign,OneStageDesign-method TwoStageDesign,OneStageDesignSurvival-method
One-stage designs for time-to-event endpointsOneStageDesignSurvival-class
Pearson's chi-squared test for contingency tablesget_tau_Pearson2xK Pearson2xK Pearson2xK-class
Plot 'TwoStageDesign' with optional set of conditional scoresplot,TwoStageDesign-method
Univariate discrete point mass priorsPointMassPrior PointMassPrior-class
Compute posterior distributionposterior posterior,DataDistribution,ContinuousPrior,numeric-method posterior,DataDistribution,PointMassPrior,numeric-method
Predictive CDFpredictive_cdf predictive_cdf,DataDistribution,ContinuousPrior,numeric-method predictive_cdf,DataDistribution,PointMassPrior,numeric-method
Predictive PDFpredictive_pdf predictive_pdf,DataDistribution,ContinuousPrior,numeric-method predictive_pdf,DataDistribution,PointMassPrior,numeric-method
Printing an optimization resultprint print.adoptrOptimizationResult
Univariate prior on model parameterPrior Prior-class
Probability density functionprobability_density_function probability_density_function,Binomial,numeric,numeric,numeric-method probability_density_function,ChiSquared,numeric,numeric,numeric-method probability_density_function,NestedModels,numeric,numeric,numeric-method probability_density_function,Normal,numeric,numeric,numeric-method probability_density_function,Student,numeric,numeric,numeric-method probability_density_function,Survival,numeric,numeric,numeric-method
Scoresevaluate evaluate,IntegralScore,TwoStageDesign-method expected expected,ConditionalScore-method Scores
Draw samples from a two-stage designsimulate,TwoStageDesign,numeric-method
Student's t data distributionquantile,Student-method simulate,Student,numeric-method Student Student-class
Create a collection of constraintsConstraintCollection evaluate,ConstraintsCollection,TwoStageDesign-method subject_to
Log-rank testquantile,Survival-method simulate,Survival,numeric-method Survival Survival-class
SurvivalDesignGroupSequentialDesign,GroupSequentialDesign-method OneStageDesign,OneStageDesign-method SurvivalDesign SurvivalDesign,GroupSequentialDesign-method SurvivalDesign,OneStageDesign-method SurvivalDesign,TwoStageDesign-method TwoStageDesign,TwoStageDesign-method
Switch between numeric and S4 class representation of a designtunable_parameters tunable_parameters,TwoStageDesign-method update,OneStageDesign-method update,TwoStageDesign-method
Two-stage designssummary,TwoStageDesign-method TwoStageDesign TwoStageDesign,numeric-method TwoStageDesign-class
Two-stage design for time-to-event-endpointsTwoStageDesignSurvival-class
Distribution class of a squared normal distributionget_tau_ZSquared ZSquared ZSquared-class