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

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 (arm64): OptSig_2.1.tgz, r-oldrel (arm64): OptSig_2.1.tgz, r-release (x86_64): OptSig_2.1.tgz, r-oldrel (x86_64): OptSig_2.1.tgz
Old sources: OptSig archive


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