alphaN: Set Alpha Based on Sample Size Using Bayes Factors
Sets the alpha level for coefficients in a regression model
as a decreasing function of the sample size through the use of
Jeffreys' Approximate Bayes factor. You tell alphaN() your sample
size, and it tells you to which value you must lower alpha to avoid
Lindley's Paradox. For details, see Wulff and Taylor (2024)
<doi:10.1177/14761270231214429>. Alpha can also be calibrated to the
effect-size and moment Bayes factors of Klauer, Meyer-Grant, and
Kellen (2025) <doi:10.3758/s13423-024-02612-2>, which center the
alternative hypothesis on an effect size of your choosing.
| Version: |
0.3.0 |
| Depends: |
R (≥ 4.0) |
| Suggests: |
BayesFactor, JustifyAlpha, knitr, pwrss, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Published: |
2026-07-27 |
| DOI: |
10.32614/CRAN.package.alphaN |
| Author: |
Jesper Wulff 
[aut, cre],
Luke Taylor [aut] |
| Maintainer: |
Jesper Wulff <jwulff at econ.au.dk> |
| BugReports: |
https://github.com/jespernwulff/alphaN/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/jespernwulff/alphaN,
https://jespernwulff.github.io/alphaN/ |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
alphaN citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
alphaN results |
Documentation:
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