This package is considered a duplicate. The official version of this package is found at:https://gravesti.r-universe.dev/psborrow

Package: psborrow 0.2.4

Isaac Gravestock

psborrow: Bayesian Dynamic Borrowing with Propensity Score

A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.

Authors:Isaac Gravestock [cre, ctb], Craig Gower-Page [aut], Matt Secrest [ctb], Yichen Lu [aut], Aijing Lin [aut], F. Hoffmann-La Roche AG [cph, fnd]

psborrow_0.2.4.tar.gz
psborrow_0.2.4.zip(r-4.7)psborrow_0.2.4.zip(r-4.6)psborrow_0.2.4.zip(r-4.5)
psborrow_0.2.4.tgz(r-4.6-any)psborrow_0.2.4.tgz(r-4.5-any)
psborrow_0.2.4.tar.gz(r-4.7-any)psborrow_0.2.4.tar.gz(r-4.6-any)
psborrow_0.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
psborrow/json (API)
NEWS

# Install 'psborrow' in R:

install.packages('psborrow', repos = c('https://pharmaverse.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/genentech/psborrow/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:

jags cpp

4.08 score 4 scripts 579 downloads 18 exports 45 dependencies

Last updated from:34c21319ea. Checks:9 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	267
source / vignettes	OK	191
linux-release-x86_64	OK	260
macos-release-arm64	OK	192
macos-oldrel-arm64	OK	133
windows-devel	OK	247
windows-release	OK	348
windows-oldrel	OK	228
wasm-release	OK	125

Exports:apply_mcmc extract_samples get_summary match_cov plot_bias plot_hr plot_mse plot_power plot_type1error run_mcmc run_mcmc_p set_clin set_cov set_event set_n set_prior simu_cov simu_time

Dependencies:backports chk cli coda codetools cpp11 data.table doParallel dplyr farver foreach formatR futile.logger futile.options generics ggplot2 glue gtable isoband iterators labeling lambda.r lattice lifecycle magrittr MatchIt Matrix mvtnorm pillar pkgconfig R6 RColorBrewer Rcpp RcppProgress rjags rlang S7 scales survival tibble tidyselect utf8 vctrs viridisLite withr

Analysing data with psborrow

Rendered fromanalysis.html.asisusingR.rsp::asison May 09 2026.

Last update: 2022-05-16
Started: 2022-05-16

User Guide: Dynamic Borrowing with psborrow

Rendered fromuser_guide.html.asisusingR.rsp::asison May 09 2026.

Last update: 2022-05-16
Started: 2022-05-16

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
S4 Class for specifying parameters for enrollment time, drop-out pattern and analysis start time	.clinClass .clinClass-class
S4 Class for setting up covariates	.covClass .covClass-class
S4 Class for setting parameters for time-to-events	.eventClass .eventClass-class
S4 Class for specifying prior distributions and predictors for MCMC methods	.priorClass .priorClass-class
Fit Dynamic Borrowing MCMC Model	apply_mcmc extract_samples summary.apply_mcmc
Concatenate multiple '.covClasss' classes	c,.covClass-method
Concatenate multiple '.priorClasss' class	c,.priorClass-method
Fix Column Names	fix_col_names
Generate summary statistics of a simulation scenario	get_summary
Check if user is in psborrow development environment	is_psborrow_dev
Match	match_cov
Plot bias	plot_bias
Plot mean posterior hazard ratio between treatment and control	plot_hr
Plot mean squared error (MSE)	plot_mse
Plot power	plot_power
Plot type 1 error	plot_type1error
Conditional Message	ps_message
Generate summary statistics for the MCMC chains	rej_est
Run MCMC for multiple scenarios with provided data	run_mcmc
Run MCMC for multiple scenarios with provided data with parallel processing	run_mcmc_p
Specify parameters for enrollment time, drop-out pattern and analysis start time	set_clin
Set up covariates	set_cov
Set up time-to-events	set_event
Simulate external trial indicator and treatment arm indicator	set_n
Specify prior distributions and predictors for MCMC methods	set_prior
Simulate covariates	simu_cov simu_cov,matrix-method
Simulate time-to-events for multiple scenarios	simu_time