DesignCTPB: Design Clinical Trials with Potential Biomarker Effect

Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.

Version: 1.1.3
Depends: R (≥ 3.5.0)
Imports: reticulate, mnormt, fields, plotly, dplyr
Suggests: knitr, rmarkdown
Published: 2021-09-21
DOI: 10.32614/CRAN.package.DesignCTPB
Author: Yitao Lu ORCID iD [aut, cre], Belaid Moa [aut], Julie Zhou [aut], Li Xing ORCID iD [aut], Xuekui Zhang ORCID iD [aut]
Maintainer: Yitao Lu <yitaolu at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:, Y Lu (2020) <doi:10.1002/sim.8868>
NeedsCompilation: no
SystemRequirements: OpenSSL(>= 1.0.1), NVIDIA CUDA GPU with compute capability 3.0 or above and NVIDIA CUDA Toolkit 9.0 or above
Citation: DesignCTPB citation info
Materials: README
CRAN checks: DesignCTPB results


Reference manual: DesignCTPB.pdf
Vignettes: DesignCTPB


Package source: DesignCTPB_1.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): DesignCTPB_1.1.3.tgz, r-oldrel (arm64): DesignCTPB_1.1.3.tgz, r-release (x86_64): DesignCTPB_1.1.3.tgz, r-oldrel (x86_64): DesignCTPB_1.1.3.tgz
Old sources: DesignCTPB archive


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