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ppseq: Design Clinical Trials using Sequential Predictive Probability Monitoring

Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.

Version: 0.2.4
Depends: R (≥ 3.5.0)
Imports: dplyr, furrr, ggplot2, plotly, purrr, tibble, patchwork, tidyr
Suggests: covr, gt, knitr, rmarkdown, spelling, testthat (≥ 3.0.0), vdiffr
Published: 2024-04-04
Author: Emily C. Zabor ORCID iD [aut, cre], Brian P. Hobbs [aut], Michael J. Kane ORCID iD [aut]
Maintainer: Emily C. Zabor <zabore2 at ccf.org>
BugReports: https://github.com/zabore/ppseq/issues
License: MIT + file LICENSE
URL: https://github.com/zabore/ppseq, https://www.emilyzabor.com/ppseq/
NeedsCompilation: no
Language: en-US
Citation: ppseq citation info
Materials: README NEWS
CRAN checks: ppseq results

Documentation:

Reference manual: ppseq.pdf
Vignettes: One-sample expansion cohort
Two-sample randomized trial

Downloads:

Package source: ppseq_0.2.4.tar.gz
Windows binaries: r-devel: ppseq_0.2.4.zip, r-release: ppseq_0.2.4.zip, r-oldrel: ppseq_0.2.4.zip
macOS binaries: r-release (arm64): ppseq_0.2.4.tgz, r-oldrel (arm64): ppseq_0.2.4.tgz, r-release (x86_64): ppseq_0.2.4.tgz, r-oldrel (x86_64): ppseq_0.2.4.tgz
Old sources: ppseq archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=ppseq to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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