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, Choosing the Level of Significance: A Decision-Theoretic Approach (December 18, 2017), available at SSRN: <https://ssrn.com/abstract=2652773> or <doi:10.2139/ssrn.2652773>. See also Kim and Ji (2015) <doi:10.1016/j.jempfin.2015.08.006>.
| Version: | 1.0 |
| Imports: | pwr |
| Published: | 2017-12-21 |
| 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_1.0.tar.gz |
| Windows binaries: | r-devel: OptSig_1.0.zip, r-release: OptSig_1.0.zip, r-oldrel: OptSig_1.0.zip |
| OS X El Capitan binaries: | r-release: OptSig_1.0.tgz |
| OS X Mavericks binaries: | r-oldrel: OptSig_1.0.tgz |
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