Package: baskwrap 1.0.3

baskwrap: Wrapper Package for Several Basket Trial R Packages

A simple interface to switch between two methods for calculating basket trial characteristics, numerical integration ("exact") and Monte Carlo simulation ("simulated") for the basket trial design by Fujikawa et al. 2020 <doi:10.1002/bimj.201800404>. The exact implementation is from the 'baskexact' package, see Baumann (2024) <doi:10.1016/j.softx.2024.101793>. The simulated implementation is from the 'basksim' package, which was developed for Baumann et al. (2024) <doi:10.1080/19466315.2024.2402275>. The package's syntax is compatible with the 'basksim' syntax and easily extendable.

Authors:Lukas D Sauer [aut, cre]

baskwrap_1.0.3.tar.gz
baskwrap_1.0.3.zip(r-4.7)baskwrap_1.0.3.zip(r-4.6)baskwrap_1.0.3.zip(r-4.5)
baskwrap_1.0.3.tgz(r-4.6-any)baskwrap_1.0.3.tgz(r-4.5-any)
baskwrap_1.0.3.tar.gz(r-4.7-any)baskwrap_1.0.3.tar.gz(r-4.6-any)
baskwrap_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
baskwrap/json (API)

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

Bug tracker:https://github.com/lukasdsauer/baskwrap/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

basket-trialsbayesian-statisticsclinical-trialsjagscpp

4.08 score 1 stars 1 packages 3 scripts 546 downloads 22 exports 46 dependencies

Last updated from:60835efed1. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK218
source / vignettesOK189
linux-release-x86_64OK213
macos-release-arm64OK185
macos-oldrel-arm64OK196
windows-develOK185
windows-releaseOK190
windows-oldrelOK186
wasm-releaseOK118

Exports:basket_testcheck_mon_betweencheck_mon_withinconvert_to_baskexactconvert_to_basksimconvert_to_fujikawa_xecdestimget_detailsget_scenariosis_baskexact_designopt_designplot_weightspowset_backendsetup_fujikawa_xtoerweights_fujikawa_tunedweights_hldweights_hld_vanillaweights_jsdweights_jsd_vanilla

Dependencies:arrangementsbackportsbaskexactbasksimbhmbasketcheckmateclicodacodetoolscpp11digestdoFuturedoRNGextraDistrfarverforeachfuturefuture.applyggplot2globalsgluegmpgtableHDIntervalisobanditeratorslabelinglatticelifecyclelistenvmagrittrparallellyprogressrpurrrR6RColorBrewerRcppRcppArmadillorjagsrlangrngtoolsS7scalesvctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Test for the results of a basket trialbasket_test
Test for the results of a basket trialbasket_test.fujikawa_x
Check Within- And Between-Trial Monotonicity Of A Basket Trial Designcheck_mon_between check_mon_between.fujikawa_x check_mon_within check_mon_within.fujikawa_x
Class conversionsconvert_to_baskexact convert_to_basksim convert_to_fujikawa_x
Calculate the Expected Number of Correct Decisions for a Basket Trial Designecd ecd.default
Calculate the Expected Number of Correct Decisions for Fujikawa et al.'s Basket Trial Designecd.fujikawa_x
Calculate the Posterior Mean and Mean Squared Error for a Basket Trial Designestim estim.default estim.fujikawa_x
Get Details of a Basket Trial Simulation with Fujikawa's Designget_details.fujikawa_x
Generate a matrix of default outcome scenariosget_scenarios
Check whether an R object is a 'baskexact' design.is_baskexact_design
Optimize a Basket Trial Designopt_design opt_design.default
Optimize Fujikawa et al.'s Basket Trial Designopt_design.fujikawa_x
Plot Weights of a Basket Trial Designplot_weights
Plot Weight Functions of Fujikawa et al.'s Basket Trial Designplot_weights.fujikawa_x
Calculate the Power for a Basket Trial Designpow pow.default
Calculate the Power for a Fujikawa et al.'s Basket Trial Designpow.fujikawa_x
Set the backend of a Fujikawa designset_backend
Set baskexact design objectset_design_exact
Set up a Fujikawa design object with flexible backendsetup_fujikawa_x
Calculate the Type 1 Error Rate for a Basket Trial Designtoer toer.default
Calculate the Type 1 Error Rate for a Fujikawa et al.'s Basket Trial Designtoer.fujikawa_x
Further weight functionsweights_fujikawa_tuned weights_hld weights_hld_vanilla weights_jsd weights_jsd_vanilla