InteractionPoweR: Power Analyses for Interaction Effects in Cross-Sectional
Regressions
Power analysis for regression models which test the interaction of
two or three independent variables on a single dependent variable. Includes options
for correlated interacting variables and specifying variable reliability.
Two-way interactions can include continuous, binary, or ordinal variables.
Power analyses can be done either analytically or via simulation. Includes
tools for simulating single data sets and visualizing power analysis results.
The primary functions are power_interaction_r2() and power_interaction() for two-way
interactions, and power_interaction_3way_r2() for three-way interactions.
Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR,
Olino TM (2023). "Tutorial: Power analyses for interaction effects in
cross-sectional regressions." <doi:10.1177/25152459231187531>.
| Version: |
0.2.2 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
dplyr, parallel, doParallel, foreach, ggplot2, polynom, chngpt, rlang, tidyr, stats, ggbeeswarm, Matrix |
| Published: |
2024-07-09 |
| DOI: |
10.32614/CRAN.package.InteractionPoweR |
| Author: |
David Baranger 
[aut, cre] (website: davidbaranger.com),
Brandon Goldstein [ctb],
Megan Finsaas [ctb],
Thomas Olino [ctb],
Colin Vize [ctb],
Don Lynam [ctb] |
| Maintainer: |
David Baranger <dbaranger at gmail.com> |
| BugReports: |
https://github.com/dbaranger/InteractionPoweR/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://dbaranger.github.io/InteractionPoweR/,
https://doi.org/10.1177/25152459231187531 |
| NeedsCompilation: |
no |
| Citation: |
InteractionPoweR citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
InteractionPoweR results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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