Package: OptGS 1.2

OptGS: Near-Optimal Group-Sequential Designs for Continuous Outcomes

Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 'OptGS' provides functions to quickly search for near-optimal group-sequential designs for normally distributed outcomes. The methods used are described in Wason, JMS (2015) <doi:10.18637/jss.v066.i02>.

Authors:James Wason [aut, cre], John Burkardt [ctb], R O'Neill [ctb]

OptGS_1.2.tar.gz
OptGS_1.2.zip(r-4.5)OptGS_1.2.zip(r-4.4)OptGS_1.2.zip(r-4.3)
OptGS_1.2.tgz(r-4.5-x86_64)OptGS_1.2.tgz(r-4.5-arm64)OptGS_1.2.tgz(r-4.4-x86_64)OptGS_1.2.tgz(r-4.4-arm64)OptGS_1.2.tgz(r-4.3-x86_64)OptGS_1.2.tgz(r-4.3-arm64)
OptGS_1.2.tar.gz(r-4.5-noble)OptGS_1.2.tar.gz(r-4.4-noble)
OptGS_1.2.tgz(r-4.4-emscripten)OptGS_1.2.tgz(r-4.3-emscripten)
OptGS.pdf |OptGS.html✨
OptGS/json (API)

# Install 'OptGS' in R:
install.packages('OptGS', repos = c('https://jmswason.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 4 scripts 557 downloads 2 exports 0 dependencies

Last updated 1 years agofrom:bc362fc2a1. Checks:12 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 20 2025
R-4.5-win-x86_64OKMar 20 2025
R-4.5-mac-x86_64OKMar 20 2025
R-4.5-mac-aarch64OKMar 20 2025
R-4.5-linux-x86_64OKMar 20 2025
R-4.4-win-x86_64OKMar 20 2025
R-4.4-mac-x86_64OKMar 20 2025
R-4.4-mac-aarch64OKMar 20 2025
R-4.4-linux-x86_64OKMar 20 2025
R-4.3-win-x86_64OKMar 20 2025
R-4.3-mac-x86_64OKMar 20 2025
R-4.3-mac-aarch64OKMar 20 2025

Exports:optgspowerfamily

Dependencies: