OptSig: Optimal Level of Significance for Regression and Other Statistical Tests

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


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