Package: baskexact 1.0.1

Lukas Baumann

baskexact: Analytical Calculation of Basket Trial Operating Characteristics

Analytically calculates the operating characteristics of single-stage and two-stage basket trials with equal sample sizes using the power prior design by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988> and the design by Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>.

Authors:Lukas Baumann [aut, cre]

baskexact_1.0.1.tar.gz
baskexact_1.0.1.tar.gz(r-4.5-noble)baskexact_1.0.1.tar.gz(r-4.4-noble)
baskexact_1.0.1.tgz(r-4.4-emscripten)baskexact_1.0.1.tgz(r-4.3-emscripten)
baskexact.pdf |baskexact.html
baskexact/json (API)

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

Peer review:

Bug tracker:https://github.com/lbau7/baskexact/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

openblascpp

3.52 score 11 scripts 265 downloads 24 exports 43 dependencies

Last updated 8 months agofrom:6ce99eafc9. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 06 2024
R-4.5-linux-x86_64NOTEDec 06 2024

Exports:adjust_lambdabasket_testcheck_mon_betweencheck_mon_withinecdessestimget_scenariosglobalweights_diffglobalweights_fixinterim_posteriorinterim_postpredopt_designplot_weightspowsetupOneStageBasketsetupTwoStageBaskettoerweights_cppweights_fujikawaweights_jsdweights_mmlweights_poolweights_separate

Dependencies:arrangementsclicodetoolscolorspacedigestdoFutureextraDistrfansifarverforeachfuturefuture.applyggplot2globalsgluegmpgtableisobanditeratorslabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmeparallellypillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr

Extending baskexact

Rendered fromv2_Extending_baskexact.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2024-03-14
Started: 2024-03-14

Introduction to baskexact

Rendered fromv1_Introduction.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2024-03-14
Started: 2024-03-14

Reproduce Results From Fujikawa et al. (2020)

Rendered fromv3_Reproduce_Fujikawa.Rmdusingknitr::rmarkdownon Dec 06 2024.

Last update: 2024-03-14
Started: 2024-03-14

Readme and manuals

Help Manual

Help pageTopics
Adjust Lambdaadjust_lambda adjust_lambda,OneStageBasket-method adjust_lambda,TwoStageBasket-method
Test for the Results of a Basket Trialbasket_test basket_test,OneStageBasket-method
Check Between-Trial Monotonicitycheck_mon_between check_mon_between,OneStageBasket-method
Check Within-Trial Monotonicitycheck_mon_within check_mon_within,OneStageBasket-method
Expected number of correct decisionsecd ecd,OneStageBasket-method ecd,TwoStageBasket-method
Expected Sample Sizeess ess,TwoStageBasket-method
Posterior Mean and Mean Squared Errorestim estim,OneStageBasket-method estim,TwoStageBasket-method
Create a Scenario Matrixget_scenarios
Global Weights Based on Response Rate Differencesglobalweights_diff
Fixed Global Weightsglobalweights_fix
Interim analysis based on the posterior probabilityinterim_posterior interim_posterior,TwoStageBasket-method
Interim analysis based on the posterior predictive probabilityinterim_postpred interim_postpred,TwoStageBasket-method
Class OneStageBasketOneStageBasket OneStageBasket-class
Optimize a Basket Designopt_design opt_design,OneStageBasket-method opt_design,TwoStageBasket-method
Plot Weight Functionsplot_weights plot_weights,OneStageBasket-method
Powerpow pow,OneStageBasket-method pow,TwoStageBasket-method
Setup OneStageBasketsetupOneStageBasket
Setup TwoStageBasketsetupTwoStageBasket
Type 1 Error Ratetoer toer,OneStageBasket-method toer,TwoStageBasket-method
Class TwoStageBasketTwoStageBasket TwoStageBasket-class
Weights Based on the Calibrated Power Priorweights_cpp weights_cpp,OneStageBasket-method weights_cpp,TwoStageBasket-method
Weights Based on Fujikawa et al.'s Designweights_fujikawa weights_fujikawa,OneStageBasket-method weights_fujikawa,TwoStageBasket-method
Weights Based on the Jensen-Shannon Divergenceweights_jsd weights_jsd,OneStageBasket-method weights_jsd,TwoStageBasket-method
Weights Based on the Marginal Maximum Likelihoodweights_mml weights_mml,OneStageBasket-method weights_mml,TwoStageBasket-method
Pooled Analysisweights_pool weights_pool,OneStageBasket-method weights_pool,TwoStageBasket-method
Separate Analysis in Each Basketweights_separate weights_separate,OneStageBasket-method weights_separate,TwoStageBasket-method