Calculates the optimal level of significance 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, Jae H. and Choi, In, 2020, Choosing the Level of Significance: A Decision-Theoretic Approach, Abacus. See also Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician.
| Version: | 2.1 |
| Imports: | pwr |
| Published: | 2020-04-18 |
| Author: | Jae H. Kim |
| Maintainer: | Jae H. Kim <J.Kim at latrobe.edu.au> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | OptSig results |
| Reference manual: | OptSig.pdf |
| Package source: | OptSig_2.1.tar.gz |
| Windows binaries: | r-devel: OptSig_2.1.zip, r-release: OptSig_2.1.zip, r-oldrel: OptSig_2.1.zip |
| macOS binaries: | r-release: OptSig_2.1.tgz, r-oldrel: OptSig_2.1.tgz |
| Old sources: | OptSig archive |
Please use the canonical form https://CRAN.R-project.org/package=OptSig to link to this page.