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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 |
| DOI: | 10.32614/CRAN.package.OptSig |
| 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 |
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