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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.2 |
rolling linux/jammy R-4.5 | OptSig_2.2.tar.gz |
99.8 KiB |
2.2 |
rolling linux/noble R-4.5 | OptSig_2.2.tar.gz |
99.8 KiB |
2.2 |
rolling source/ R- | OptSig_2.2.tar.gz |
19.4 KiB |
2.2 |
latest linux/jammy R-4.5 | OptSig_2.2.tar.gz |
99.8 KiB |
2.2 |
latest linux/noble R-4.5 | OptSig_2.2.tar.gz |
99.8 KiB |
2.2 |
latest source/ R- | OptSig_2.2.tar.gz |
19.4 KiB |
2.2 |
2026-04-26 source/ R- | OptSig_2.2.tar.gz |
19.4 KiB |
2.2 |
2026-04-23 source/ R- | OptSig_2.2.tar.gz |
19.4 KiB |
2.2 |
2026-04-09 windows/windows R-4.5 | OptSig_2.2.zip |
102.8 KiB |
2.2 |
2025-04-20 source/ R- | OptSig_2.2.tar.gz |
19.4 KiB |