R Observatory: Metrics for BayesCTDesign version 0.6.1

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

BayesCTDesign

0.6.1Package record

Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data

0packages depend

4.3Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Barry EgglestonCRAN page ↗GitHub ↗Issues ↗Declared funder: Diane Catellier

A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) doi:10.18637/jss.v100.i21.

Changes in 0.6.1

Changes:

For piecewise exponential model (pwe), changed the code to call eha::pchreg() instead of eha::phreg() as instructed by the eha package.
Removed the deprecated functions print_table() and plot_table().

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("BayesCTDesign", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("begglest/BayesCTDesign")

Development version from begglest/BayesCTDesign. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-bayesctdesign

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-BayesCTDesign

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-BayesCTDesign

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 1.9 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.6.1

README· In the repository, left out of the releaseVignettesNoNEWSYes · 33% structured · mentions 0.6.1Citation fileYes · cites a publicationWebsitenot declared in DESCRIPTION

Cites a JSS article

Help pages: 10
Examples that run: 100%10 run in a plain check
Exported functions with examples: 100%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 0%

How these are measured

Downloads

4.3K

CRAN downloads in the past year

Rank #8,058 · ~12/day · ~360/mo

Releases R versions

Daily download trend is not available in this view yet.

38430 days

1.1K90 days

4.3K1 year

Compare downloads with other packages →

Also on116 r2u14 autocran66 c2d4u

How downloads are counted

Repository

begglest/bayesctdesign ↗Account: begglest

2Stars

1Forks

0Open issues

0Open PRs

0Releases

6Commits

1Commit identities

6 commits · Last activity 2021-11-28

Responsiveness

How these are sampled

PRs merged (all time): not measured
Median days to close, 50 most recently updated closed issues: 63
Median days to close, 50 most recently updated closed PRs: not measured

Stars over time

2023-11-19 · 22026-07-07 · 2

How these are measured

PracticesBeta

3 development-tooling and community-health practices detected across 3 families in the upstream repository

Checks run against github.com/begglest/bayesctdesign on 2026-10-18.

Docs source (1)

README.Rmd

CRAN release process (1)

cran-comments.md

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies

3 external dependencies (excludes base and recommended)

Installing BayesCTDesign adds 24 packages (13 with compiled code, 29 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, eha 2.0 MB.

Depends (1)

R >= 3.5.0

Imports (5)

eha ggplot2survivalreshape2stats

LinkingTo (0)

none

Suggests (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (5)

Barry Eggleston

Maintainer since 0.5.0 (2018), Author

Funder, rth, Author

Author

Author

Author

Releases (3)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
R
R 4.2.0 released · 2022-04-22
0.6.1Latest
record
2021-11-30 · current release · diff ↗ ·
removed 2 exports
plot_table, print_table
R
R 4.1.0 released · 2021-05-18
R
R 4.0.0 released · 2020-04-24
0.6.0
record
2019-08-02 · diff ↗
R
R 3.6.0 released · 2019-04-26
0.5.0
record
2018-08-14
R
R 3.5.0 released · 2018-04-23

Package metadata

First published: 2018-08-14
Total releases: 3 / 8 yrs
License: GPL-3 OSI
Minimum R: ≥ 3.5.0
Download size: 73 KB

Appears in task views

Bayesian Inference Clinical Trial Design, Monitoring, Analysis and Reporting

Cite

Cite this package

Run in R for the authors' entry:

citation("BayesCTDesign")

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.18637/jss.v100.i21.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for BayesCTDesign version 0.6.1 [Data set]. HJJB, LLC. Data release v2026-10-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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