Package: beastt 0.0.3.9000

Christina Fillmore

beastt: Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials

Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. This methodology based on Psioda et al (2025) <doi:10.1080/10543406.2025.2489285>.

Authors:Christina Fillmore [aut, cre], Nate Bean [aut], Abi Terry [aut], Ben Arancibia [aut], GlaxoSmithKline Research & Development Limited [cph, fnd], Trustees of Columbia University [cph]

beastt_0.0.3.9000.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
beastt/json (API)
NEWS

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

Bug tracker:https://github.com/gsk-biostatistics/beastt/issues

Pkgdown/docs site:https://gsk-biostatistics.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • binary_sim_df - Binary Simulation Data
  • ex_binary_df - External Binary Control Data for Propensity Score Balancing
  • ex_norm_df - External Normal Control Data for Propensity Score Balancing
  • ex_tte_df - External Time-to-Event Control Data for Propensity Score Balancing
  • int_binary_df - Internal Binary Data for Propensity Score Balancing
  • int_norm_df - Internal Normal Data for Propensity Score Balancing
  • int_tte_df - Internal Time-to-Event Control Data for Propensity Score Balancing
  • tte_sim_df - Time-to-Event Simulation Data

On CRAN:

Conda:

cpp

6.30 score 6 stars 14 scripts 601 downloads 32 exports 99 dependencies

Last updated from:d265759395. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK367
linux-devel-x86_64OK336
source / vignettesOK476
linux-release-arm64OK385
linux-release-x86_64OK367
macos-release-arm64OK242
macos-release-x86_64OK483
macos-oldrel-arm64OK375
macos-oldrel-x86_64OK510
windows-develOK502
windows-releaseOK431
windows-oldrelOK400
wasm-releaseFAIL170

Exports:approx_mvn_at_timeavg_distbootstrap_covcalc_cond_binarycalc_cond_weibullcalc_post_betacalc_post_normcalc_post_weibullcalc_power_prior_betacalc_power_prior_normcalc_power_prior_weibullcalc_prop_scrcalc_study_durationglanceinv_logitis_prop_scrmix_meansmix_sigmasplot_distprop_scr_cloudprop_scr_densprop_scr_histprop_scr_loverescale_psrobustify_mvnormrobustify_normsim_accrualsim_pw_const_hazsim_weib_phsweet_spot_plottidytrim_ps

Dependencies:abindaskpassbackportsbase64encBHbslibcachemcallrcheckmatechkclicobaltcpp11crosstalkcurldata.tabledescdigestdistributionaldplyrevaluatefarverfastmapfontawesomefsgenericsggdistggplot2gluegridExtragtablehighrhtmltoolshtmlwidgetshttrinlineisobandjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecycleloomagrittrMASSMatrixmatrixStatsmemoisemimemixtoolsnlmenumDerivopensslotelpillarpkgbuildpkgconfigplotlyposteriorprocessxpromisespspurrrquadprogQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrmarkdownrstanrstantoolsS7sassscalessegmentedStanHeadersstringistringrsurvivalsystensorAtibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Binary Outcome

Rendered frombinary.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2026-04-20
Started: 2024-06-14

Normal Outcome (Known SD)

Rendered fromcontinuous.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2025-05-12
Started: 2024-02-08

Time-to-Event Outcome

Rendered fromtte.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2026-04-20
Started: 2024-12-10

Readme and manuals

Help Manual

Help pageTopics
The 'beastt' package.beastt-package beastt
Approximate Multivariate Normal Distribution as Beta at a Specific Timeapprox_mvn_at_time
Calculate Average Distribution from Multiple Distributional Objectsavg_dist
Binary Simulation Databinary_sim_df
Bootstrap Covariate Databootstrap_cov
Calculate Conditional Drift and Treatment Effect for Binary Outcome Modelscalc_cond_binary
Calculate Conditional Drift and Treatment Effect for Time-to-Event Outcome Modelscalc_cond_weibull
Calculate Posterior Betacalc_post_beta
Calculate Posterior Normalcalc_post_norm
Calculate Posterior Weibullcalc_post_weibull
Calculate Power Prior Betacalc_power_prior_beta
Calculate Power Prior Normalcalc_power_prior_norm
Calculate Power Prior Weibullcalc_power_prior_weibull
Create a Propensity Score Objectcalc_prop_scr
Calculate the Analysis Time Based on a Target Number of Events and/or Target Follow-up Timecalc_study_duration
External Binary Control Data for Propensity Score Balancingex_binary_df
External Normal Control Data for Propensity Score Balancingex_norm_df
External Time-to-Event Control Data for Propensity Score Balancingex_tte_df
Internal Binary Data for Propensity Score Balancingint_binary_df
Internal Normal Data for Propensity Score Balancingint_norm_df
Internal Time-to-Event Control Data for Propensity Score Balancingint_tte_df
Inverse Logit Functioninv_logit
Test If Propensity Score Objectis_prop_scr
Extract Means of Mixture Componentsmix_means
Extract Standard Deviations of Mixture Componentsmix_sigmas
Plot Distributionplot_dist
Propensity Score Cloud Plotprop_scr_cloud
Density of the Propensity Score Objectprop_scr_dens
Histogram of the Propensity Score Objectprop_scr_hist
Love Plot of the Absolute Standardized Mean Differencesprop_scr_love
Rescale a 'prop_scr' objectrescale_ps
Robustify Multivariate Normal Distributionsrobustify_mvnorm
Robustify Normal Distributionsrobustify_norm
Simulate Participant Accrual Timessim_accrual
Simulate Event Times for Each Individual from a Piecewise Constant Hazard Modelsim_pw_const_haz
Simulate Event Times for Each Participant from a Weibull Proportional Hazards Regression Modelsim_weib_ph
Create Sweet Spot Plots for Multiple Simulation Scenariossweet_spot_plot
Tidy a(n) prop_scr objecttidy.prop_scr
Trim a 'prop_scr' objecttrim_ps
Time-to-Event Simulation Datatte_sim_df