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-4.7-x86_64)OptGS_1.2.zip(r-4.7-arm64)OptGS_1.2.zip(r-4.6-x86_64)OptGS_1.2.zip(r-4.6-arm64)OptGS_1.2.zip(r-4.5-x86_64)
OptGS_1.2.tgz(r-4.6-x86_64)OptGS_1.2.tgz(r-4.6-arm64)OptGS_1.2.tgz(r-4.5-x86_64)OptGS_1.2.tgz(r-4.5-arm64)
OptGS_1.2.tar.gz(r-4.7-arm64)OptGS_1.2.tar.gz(r-4.7-x86_64)OptGS_1.2.tar.gz(r-4.6-arm64)OptGS_1.2.tar.gz(r-4.6-x86_64)
OptGS_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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 6 scripts 373 downloads 2 exports 0 dependencies

Last updated from:bc362fc2a1. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK76
linux-devel-x86_64OK94
source / vignettesOK148
linux-release-arm64OK72
linux-release-x86_64OK87
macos-release-arm64OK174
macos-release-x86_64OK319
macos-oldrel-arm64OK168
macos-oldrel-x86_64OK224
windows-devel-arm64OK300
windows-devel-x86_64OK109
windows-release-arm64OK315
windows-release-x86_64OK90
windows-oldrel-x86_64OK76
wasm-releaseOK81

Exports:optgspowerfamily

Dependencies: