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.

Getting started

Package details

AuthorJae H. Kim <J.Kim@latrobe.edu.au>
MaintainerJae H. Kim <J.Kim@latrobe.edu.au>
Package repositoryView on CRAN
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OptSig documentation built on April 19, 2020, 4:16 p.m.