PPQplan: Process Performance Qualification (PPQ) Plans in Chemistry, Manufacturing and Controls (CMC) Statistical Analysis

Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham.

Version: 1.1.0
Depends: R (≥ 3.2.0)
Imports: ggplot2, plotly
Suggests: knitr, rmarkdown, devtools
Published: 2020-10-08
Author: Yalin Zhu ORCID iD [aut, cre], Merck & Co., Inc. [cph]
Maintainer: Yalin Zhu <yalin.zhu at merck.com>
BugReports: https://github.com/allenzhuaz/PPQplan/issues
License: GPL-3
URL: https://allenzhuaz.github.io/PPQplan/, https://github.com/allenzhuaz/PPQplan
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: PPQplan results

Downloads:

Reference manual: PPQplan.pdf
Vignettes: PPQ Power Assessment Theoretical Results
Introduction to PPQplan
Package source: PPQplan_1.1.0.tar.gz
Windows binaries: r-devel: PPQplan_1.1.0.zip, r-release: PPQplan_1.1.0.zip, r-oldrel: PPQplan_1.1.0.zip
macOS binaries: r-release: PPQplan_1.1.0.tgz, r-oldrel: PPQplan_1.1.0.tgz
Old sources: PPQplan archive

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