The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.
| Version: | 2.2 |
| Imports: | pwr |
| Published: | 2022-07-03 |
| Author: | Jae H. Kim |
| Maintainer: | Jae H. Kim <jaekim8080 at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | OptSig results |
| Reference manual: | OptSig.pdf |
| Package source: | OptSig_2.2.tar.gz |
| Windows binaries: | r-devel: OptSig_2.2.zip, r-release: OptSig_2.2.zip, r-oldrel: OptSig_2.2.zip |
| macOS binaries: | r-release (arm64): OptSig_2.2.tgz, r-oldrel (arm64): OptSig_2.2.tgz, r-release (x86_64): OptSig_2.2.tgz, r-oldrel (x86_64): OptSig_2.2.tgz |
| Old sources: | OptSig archive |
Please use the canonical form https://CRAN.R-project.org/package=OptSig 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.